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Reyhana, Zakya, Kartika Fithriasari, Moh Atok, and Nur Iriawan. "Linking Twitter Sentiment Knowledge with Infrastructure Development." MATEMATIKA 34, no. 3 (2018): 91–102. http://dx.doi.org/10.11113/matematika.v34.n3.1142.
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Sentiment analysis is related to the automatic extraction of positive or negative opinions from the text. It is a special text mining application. It is important to classify implicit contents from citizen’s tweet using sentiment analysis. This research aimed to find out the opinion of infrastructure that sustained urban development in Surabaya, Indonesia’s second largest city. The procedures of text mining analysis were the data undergoes some preprocessing first, such as removing the link, retweet (RT), username, punctuation, digits, stopwords, case folding, and tokenizing. Then, the opinion was classified into positive and negative comments. Classification methods used in this research were support vector machine (SVM) and neural network (NN). The result of this research showed that NN classification method was better than SVM.
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Purohit, Amit. "Sentiment Analysis of Customer Product Reviews using deep Learning and Compare with other Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 233–39. http://dx.doi.org/10.22214/ijraset.2021.36202.
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Sentiment analysis is defined as the process of mining of data, view, review or sentence to Predict the emotion of the sentence through natural language processing (NLP) or Machine Learning Techniques. The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”. The process of finding user Opinion about the topic or Product or problem is called as opinion mining. Analyzing the emotions from the extracted Opinions are defined as Sentiment Analysis. The goal of opinion mining and Sentiment Analysis is to make computer able to recognize and express emotion. Using social media, E-commerce website, movies reviews such as Face book, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. Sentiment analysis in a machine learning approach in which machines classify and analyze the human’s sentiments, emotions, opinions etc. about the products. Out of the various classification models, Naïve Bayes, Support Vector Machine (SVM) and Decision Tree are used maximum times for the product analysis. The proposed approach will do better result as compare to other machine learning techniques.
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Chinedum, Amaechi, and Okeke Ogochukwu C. "A Review on Opinion Mining: Approaches, Practices and Application." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 3 (2021): 01–06. http://dx.doi.org/10.17762/ijritcc.v9i3.5456.
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Opinion Mining also known as Sentiment Analysis (SA) has recently become the focus of many researchers, because analysis of online text is useful and demanded in many different applications. Analysis of social sentiments is a trending topic in this era because users share their emotions in more suitable format with the help of micro blogging services like twitter. Twitter provides information about individual's real-time feelings through the data resources provided by persons. The essential task is to extract user's tweets and implement an analysis and survey. However, this extracted information can very helpful to make prediction about the user's opinion towards specific policies. The motive of this paper is to perform a survey on sentiment analysis algorithms that shows the utilizing of different ML and Lexicon investigation methodologies and their accuracy. Our paper also focuses on the three kinds of machine learning algorithms for Sentiment Analysis- Supervised, Unsupervised Algorithms.
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Bahrawi, Nfn. "Online Realtime Sentiment Analysis Tweets by Utilizing Streaming API Features From Twitter." Jurnal Penelitian Pos dan Informatika 9, no. 1 (2019): 53. http://dx.doi.org/10.17933/jppi.2019.090105.
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<p class="JGI-AbstractIsi">Twitter is one of the social media that has a simple and fast concept, because short messages, news or information on Twitter can be more easily digested. This social media is also widely used as an object for researchers or industry to conduct sentiment analysis in the fields of social, economic, political or other fields. Opinion mining or also commonly called sentiment analysis is the process of analyzing text to get certain information in a sentence in the form of opinion. Sentiment analysis is one of the branches of the science of Text mining where text mining is a natural language processing technique and analytical method that is applied to text data to obtain relevant information. Public opinion or sentiment in social media twitter is very dynamic and fast changing, a real time sentiment analysis system is needed and it is automatically updated continuously so that changes can always be monitored, anytime and anywhere. This research builds a system so that it can analyze sentiment from twitter social media in realtime and automatically continuously. The results of the system trial succeeded in drawing data, conducting sentiment analysis and displaying it in graphical and web-based realtime and updated automatically. Furthermore, this research will be developed with a focus on the accuracy of the algorithms used in conducting the sentiment analysis process.</p>
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Bourequat, Wasim, and Hassan Mourad. "Sentiment Analysis Approach for Analyzing iPhone Release using Support Vector Machine." International Journal of Advances in Data and Information Systems 2, no. 1 (2021): 36–44. http://dx.doi.org/10.25008/ijadis.v2i1.1216.
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Sentiment analysis is a process of understanding, extracting, and processing textual data automatically to get sentiment information contained in a comment sentence on Twitter. Sentiment analysis needs to be done because the use of social media in society is increasing so that it affects the development of public opinion. Therefore, it can be used to analyze public opinion by applying data science, one of which is Natural Language Processing (NLP) and Text Mining or also known as text analytics. The stages of the overall method used in this study are to do text mining on the Twitter site regarding iPhone Release with methods of scraping, labeling, preprocessing (case folding, tokenization, filtering), TF-IDF, and classification of sentiments using the Support Vector Machine. The Support Vector Machine is widely used as a baseline in text-related tasks with satisfactory results, on several evaluation matrices such as accuracy, precision, recall, and F1 score yielding 89.21%, 92.43%, 95.53%, and 93.95, respectively.
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Jain, Kirti. "Sentiment Analysis on Twitter Airline Data." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3767–70. http://dx.doi.org/10.22214/ijraset.2021.35807.
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Sentiment analysis, also known as sentiment mining, is a submachine learning task where we want to determine the overall sentiment of a particular document. With machine learning and natural language processing (NLP), we can extract the information of a text and try to classify it as positive, neutral, or negative according to its polarity. In this project, We are trying to classify Twitter tweets into positive, negative, and neutral sentiments by building a model based on probabilities. Twitter is a blogging website where people can quickly and spontaneously share their feelings by sending tweets limited to 140 characters. Because of its use of Twitter, it is a perfect source of data to get the latest general opinion on anything.
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Ritika Siril Paul, Yazala, and Dilipkumar A. Borikar. "An Approach To Twitter Sentiment Analysis Over Hadoop." International Journal of Engineering & Technology 7, no. 4.5 (2018): 374. http://dx.doi.org/10.14419/ijet.v7i4.5.20110.
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Sentiment analysis is the process of identifying people’s attitude and emotional state from the language they use via any social websites or other sources. The main aim is to identify a set of potential features in the review and extract the opinion expressions of those features by making full use of their associations. The Twitter has now become a routine for the people around the world to post thousands of reactions and opinions on every topic, every second of every single day. It’s like one big psychological database that’s constantly being updated and which can be used to analyze the sentiments of the people. Hadoop is one of the best options available for twitter data sentiment analysis and which also works for the distributed big data, streaming data, text data etc. This paper provides an efficient mechanism to perform sentiment analysis/ opinion mining on Twitter data over Hortonworks Data platform, which provides Hadoop on Windows, with the assistance of Apache Flume, Apache HDFS and Apache Hive.
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Steven, Cristian, and Wella Wella. "The Right Sentiment Analysis Method of Indonesian Tourism in Social Media Twitter." IJNMT (International Journal of New Media Technology) 7, no. 2 (2020): 102–10. http://dx.doi.org/10.31937/ijnmt.v7i2.1732.
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The growth of social media is changing the way humans communicate with each other, many people use social media such as Twitter to express opinions, experiences and other things that concern them, where things like this are often referred to as sentiments. The concept of social media is now the focus of business people to find out people's sentiments about a product or place that will become a business. Sentiment Analysis or often also called opinion mining is a computational study of people's opinions, appraisal, and emotions through entities, events and attributes owned. Sentiment analysis itself has recently become a popular topic for research because sentiment analysis can be applied in many industrial sectors, one of which is the tourism industry in Indonesia. To be able to do a sentiment analysis requires mastery of several techniques such as techniques for doing text mining, machine learning and natural language processing (NLP) to be able to process large and unstructured data coming from social media. Some methods that are often used include Naive Bayes, Neural Networks, K-Nearest Neighbor, Support Vector Machines, and Decision Tree. Because of this, this research will compare these four algorithms so that an algorithm can be used to analyze people's sentiments towards the city of Bali.
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Bahrawi, Nfn. "Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based." Journal of Information Technology and Its Utilization 2, no. 2 (2019): 29. http://dx.doi.org/10.30818/jitu.2.2.2695.
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Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research. Keywords: sentiment analysis; random forest algorithm; clasification; machine learnings.
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Karami, Amir, London S. Bennett, and Xiaoyun He. "Mining Public Opinion about Economic Issues." International Journal of Strategic Decision Sciences 9, no. 1 (2018): 18–28. http://dx.doi.org/10.4018/ijsds.2018010102.
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Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This article proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.
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Fauziyyah, Anni Karimatul. "ANALISIS SENTIMEN PANDEMI COVID19 PADA STREAMING TWITTER DENGAN TEXT MINING PYTHON." Jurnal Ilmiah SINUS 18, no. 2 (2020): 31. http://dx.doi.org/10.30646/sinus.v18i2.491.
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The impact of the novel coronavirus (COVID-19) is widespread and will likely shape community behavior for months to come. And while the humanitarian and safety-related aspects of this outbreak are top of mind globally, it’s unquestionable that social distancing, quarantining, and staying home will have a significant effect on media consumption, which could rise up to 60%, according to recent research from Nielsen’s U.S. media team. Social media, now a part of everyday life for most consumers engaged with the world digitally, became the primary source for buzz about all things COVID-19 as worries and news intensified. Sentiment analysis is applied in this study to analyze the opinions, feelings, and interests of individuals in the COVID-19. The purpose of this study is to analyze sentiment based on an opinion by classifying individual feelings such as sadness, happiness, or panic in facing a COVID-19 into sentiment level that is negative, positive or, neutral. In this paper, an open-source approach is presented where we have collected tweets from the Twitter API and then reprocessing, analyzing and, visualizing these tweets using python. Furthermore, Twitter data streaming will be processed and cleaned to parse data that can be classified based on opinion with a text mining algorithm using text blob Python. Feature extraction is done for the relationship between words by the Bigram and N-gram methods.
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Yee Liau, Bee, and Pei Pei Tan. "Gaining customer knowledge in low cost airlines through text mining." Industrial Management & Data Systems 114, no. 9 (2014): 1344–59. http://dx.doi.org/10.1108/imds-07-2014-0225.
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Purpose – The purpose of this paper is to study the consumer opinion towards the low-cost airlines or low-cost carriers (LCCs) (these two terms are used interchangeably) industry in Malaysia to better understand consumers’ needs and to provide better services. Sentiment analysis is undertaken in revealing current customers’ satisfaction level towards low-cost airlines. Design/methodology/approach – About 10,895 tweets (data collected for two and a half months) are analysed. Text mining techniques are used during data pre-processing and a mixture of statistical techniques are used to segment the customers’ opinion. Findings – The results with two different sentiment algorithms show that there is more positive than negative polarity across the different algorithms. Clustering results show that both K-Means and spherical K-Means algorithms delivered similar results and the four main topics that are discussed by the consumers on Twitter are customer service, LCCs tickets promotions, flight cancellations and delays and post-booking management. Practical implications – Gaining knowledge of customer sentiments as well as improvements on the four main topics discussed in this study, i.e. customer service, LCCs tickets promotions, flight cancellations or delays and post-booking management will help LCCs to attract more customers and generate more profits. Originality/value – This paper provides useful insights on customers’ sentiments and opinions towards LCCs by utilizing social media information.
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Andrade, Carina Sofia, and Maribel Yasmina Santos. "Sentiment Analysis with Text Mining in Contexts of Big Data." International Journal of Technology and Human Interaction 13, no. 3 (2017): 47–67. http://dx.doi.org/10.4018/ijthi.2017070104.
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The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.
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Kumar Atmakur, Vijay, and Dr P.Siva Kumar. "A prototype analysis of machine learning methodologies for sentiment analysis of social networks." International Journal of Engineering & Technology 7, no. 2.7 (2018): 963. http://dx.doi.org/10.14419/ijet.v7i2.7.11436.
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In present day’s social networking technologies are increased because of different user’s communication with each others. There are different types of networks are available in present situations like face book, twitter and LinkedIn. These are the valuable resources for data mining applications because of prevalence presents of different user’s information present in outside environment. Sentiment analysis is the process that defines attitudes, views, emotions and opinions from text, database sources and tweets. Sentiment analysis involves to categorize data based on different opinions like positive and negative or neutral reference classes. In this paper, we analyze different machine learning approaches to define sentiment analysis on social networks. This paper describes comparative analysis of existing machine learning approaches to classify text and other reference classes to evaluate different metric representations. And also this paper describes different machine learning methodologies like Naïve Bayesian, Entropy max and support vector machine (SVM) research on social network data streams. And also discuss major innovations to evaluate different procedures and challenges of analysis of sentiment or opinion mining aspects in present social networks.
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Mahani, Aestikani, and Hendro Margono. "Prediksi Sentimen Investor Pasar Modal Di Jejaring Sosial Menggunakan Text Mining." BALANCE: Economic, Business, Management and Accounting Journal 18, no. 2 (2021): 32. http://dx.doi.org/10.30651/blc.v18i2.7226.
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The decline in optimism for capital market investors is one of the financial impacts on the business world that arose from the SARS-COVID19 pandemic. This event was reflected in a decrease in trading volume followed by a sharp drop in the JCI on the Indonesia Stock Exchange starting March 2020. Thus, a slowdown in the economic recovery resulting from the pandemic is reflected in investor sentiment in the capital market. On the one hand, the rapid development of the internet in Indonesia has triggered the investor's activities in the information searching prior buy and sell securities, mostly use online platforms, which contribute to influencing investor preferences and sentiment. This study conducted a qualitative examination of the features/terms of stock investment in the capital market and collected them in a compact dictionary (lexicon). Therefore, lexicon-based investor opinion extraction was extracted from Twitter, followed by the text sentiment analysis, and forming a classification model based on Naive Bayes and Decision Tree. This research output shows that the polarity of capital market investor sentiment is optimistic with the sentiment features that often appear, namely "cuan", "bearish," "serok", "copet", "untung", "cut loss", and "nyangkut." Meanwhile, the Decision Tree classification model provides better performance.Keywords : investor, lexicon, social network, stock exchange, text miningCorrespondence to : aestikani.mahani-2019@feb.unair.ac.id Penurunan optimisme investor pasar modal adalah salah satu dampak keuangan pada dunia usaha yang timbul akibat pandemi SARS-COVID19. Hal ini tercermin dari turunnya volume perdagangan yang diikuti penurunan tajam IHSG di Bursa Efek Indonesia mulai Maret 2020. Sehingga kekhawatiran atas perlambatan pemulihan ekonomi sebagai dampak pandemi, tercermin dari sentimen investor di pasar modal. Di satu sisi, perkembangan internet di Indonesia yang pesat, memicu kecenderungan aktivitas investor dalam pencarian informasi sebelum membeli dan menjual surat berharga secara online, turut berkontribusi dalam mempengaruhi preferensi dan sentimen investor. Penelitian ini menggali ekspektasi investor yang tercermin pada sentimen investasi, dimana pasar modal sebagai salah satu barometer penting perekonomian suatu negara. Kajian ini mengeksplorasi fitur/terms investasi saham yang kerap muncul di pasar modal dan mengumpulkannya dalam kamus leksikon. Kemudian, dilakukan ekstraksi opini investor berbasis leksikon yang digali dari jejaring sosial Twitter, dilanjutkan dengan tahap text mining yaitu menganalisis sentimen, dan membentuk model klasifikasi berbasis Naive Bayes dan Decision Tree. Keluaran penelitian ini menunjukkan bahwa polaritas sentimen investor pasar modal adalah positif dengan fitur sentimen yang sering muncul yaitu “cuan”, “bearish”, “serok”, “copet”, “untung”, dan “cut loss”. Sedangkan model klasifikasi Decision Tree memberikan performansi akurasi yang kebih baik.Kata Kunci : Analisis sentimen; Investor; Leksikon; Text mining; Twitter
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Alshammari, Hamoud H. "Bag-of-Phrases (BoPh) and sentiment analysis of Arabic text in Twitter." Indian Journal of Science and Technology 13, no. 40 (2020): 4202–15. http://dx.doi.org/10.17485/ijst/v13i40.1202.
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Background/Objectives: Sentiment analysis plays main role in various text mining problems. Although, the Arabic text mining is important especially in the field of sentiment analysis, there is a paucity of research in it, especially, when it plays an important role in different issues in Arabic countries. Arabic language has many dialects that people use to express their feelings in social media. The objective of this study is to perform an experiment that follow the subjective opinion from the text. Subjective Analysis is one way that we can implement to improve the accuracy of the sentiment results in such texts in some dialects, that hide various meanings behind the words such as Saudi dialect. Methods/Statistical analysis: In this study, we manually annotated more than 8,000 tweets to have training and testing data sets with positive or negative words and phrases. Then we proposed a “Bag of Phrases” methodology to analyze the sentiments in the texts, which helped to improve the performance of sentiment analysis. Since using bag of words method is not enough in many cases, we applied a Naive Bayes algorithm to test our method. Findings: The results show that the accuracy of having True positive or True negative is about 84% comparing by using manual annotation process. The accuracy is calculated after taking into consideration the margin of error due to the manual annotation step and subjective interpretation of the texts by the annotators. Novelty/Applications: The novelty of the study is having more accurate training data set comparing with the other works in Saudi dialect for Arabic text, and proposing the BoPh concept.
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Rameshbhai, Chaudhary Jashubhai, and Joy Paulose. "Opinion mining on newspaper headlines using SVM and NLP." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 2152. http://dx.doi.org/10.11591/ijece.v9i3.pp2152-2163.
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<p>Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.</p>
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Oladipo, Francisca, Ogunsanya, F. B, Musa, A. E., Ogbuju, E. E, and Ariwa, E. "Reviewing Sentiment Analysis at the Shallow End." Transactions on Machine Learning and Artificial Intelligence 8, no. 4 (2020): 47–62. http://dx.doi.org/10.14738/tmlai.84.8274.
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The social media space has evolved into a large labyrinth of information exchange platform and due to the growth in the adoption of different social media platforms, there has been an increasing wave of interests in sentiment analysis as a paradigm for the mining and analysis of users’ opinions and sentiments based on their posts. In this paper, we present a review of contextual sentiment analysis on social media entries with a specific focus on Twitter. The sentimental analysis consists of two broad approaches which are machine learning which uses classification techniques to classify text and is further categorized into supervised learning and unsupervised learning; and the lexicon-based approach which uses a dictionary without using any test or training data set, unlike the machine learning approach.
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Wang, Yinying, and David J. Fikis. "Common Core State Standards on Twitter: Public Sentiment and Opinion Leaders." Educational Policy 33, no. 4 (2017): 650–83. http://dx.doi.org/10.1177/0895904817723739.
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The purpose of this study is to examine the public opinion on the Common Core State Standards (CCSS) on Twitter. Using Twitter Application Program Interface (API), we collected the tweets containing the hashtags #CommonCore and #CCSS for 12 months from 2014 to 2015. A Common Core corpus was created by compiling all the collected 660,051 tweets. The results of sentiment analysis suggest Twitter users expressed overwhelmingly negative sentiment toward the CCSS in all 50 states. Five topic clusters were detected by cluster analysis of the hashtag co-occurrence network. We also found that most of the opinion leaders were those who expressed negative sentiment toward the CCSS on Twitter. This study for the first time demonstrates how text mining techniques can be applied to education policy research, laying the foundation for real-time analytics of public opinion on education policies, thereby informing policymaking and implementation.
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Euis Saraswati, Yuyun Umaidah, and Apriade Voutama. "Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19." Generation Journal 5, no. 2 (2021): 109–18. http://dx.doi.org/10.29407/gj.v5i2.16125.
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Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.
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Rolliawati, Dwi, Khalid Khalid, and Indri Sudanawati Rozas. "Teknologi Opinion Mining untuk Mendukung Strategic Planning." Jurnal Teknologi Informasi dan Ilmu Komputer 7, no. 2 (2020): 293. http://dx.doi.org/10.25126/jtiik.2020721685.
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<p class="Abstrak">Banjir data di era Big Data sudah tidak bisa terelakkan lagi. Termasuk di dalamnya data yang sangat melimpah di media sosial daring. Peluang inilah yang ditangkap sebagai alasan utama pada penelitian ini. <em>Opinion mining</em> sebagai salah satu teknologi dalam mengolah data teks untuk memperoleh arah informasi dari komentar/opini masyarakat. Mengambil obyek penelitian UIN Sunan Ampel Surabaya, penelitian ini bertujuan untuk menganalisis opini masyarakat tentang kampus Islam terbesar di Surabaya. Sehingga bisa menjadi pendukung keputusan bagi pihak manajemen untuk merumuskan perencanaan strategis terwujudnya visi <em>World Class University</em>. Penelitian ini menggunakan 4009 data sampel berbahasa Indonesia yang diambil dari opini masyarakat di media sosial Twitter dalam kurun waktu dua tahun terakhir (2017 – 2018). Dari 4009 data dihasilkan 31837 jenis kata setelah melalui proses <em>stop-word removal</em>. Berdasarkan analisis <em>sentiment</em> menggunakan pendekatan Vader dan Liu yang divisualisasikan melalui grafik K-Means, dihasilkan bahwa opini publik terhadap UIN Sunan Ampel mengarah pada sentimen ’netral’ sebesar 97,54%, sedangkan sentiment positif =2,16%, dan sentiment negatif = 0,34%. Hasil tersebut membuktikan bahwa <em>Information Capital</em> tentang UIN Sunan Ampel perlu diperkuat menuju nilai “positif”. Sehingga diperlukan upaya maksimal untuk membangun <em>innovation and commercially supremacy, perception (public relation)</em> dan <em>scalability strategies</em> supaya <em>internal operation</em> bisa handal untuk ketercapaian visi misi UIN Sunan Ampel Surabaya.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Data deluge in Big Data era is inevitable, this including a very abundant data in online social media. This phenomenon was chosen as the main background reason in this research. Opinion mining is as one of the technologies in processing text data to obtain information direction from public comments/opinions. Taking the object of research at Sunan Ampel Islamic State University Surabaya, this study aims to analyze public community opinion toward the biggest Islamic campus in Surabaya. Hopefully, it would be beneficial as decisional support for management in formulating strategic planning to manifest the World Class University vision. This study uses 4009 Indonesian language sample data taken from public opinion on Twitter social media in the past two years (2017 - 2018). Out from 4009 data, 31837 types of words are obtained after going through a stop-word removal process. Based on sentiment analysis by Vader and Liu’s approach which was visualized by K-Means graphs, the finding was that 97,54% of public opinion toward Sunan Ampel Islamic State University Surabaya led to a 'neutral' sentiment, while positive = 2,16% and negative=0,34%. These results prove that Information Capital about Sunan Ampel UIN needs to be strengthened towards "positive" image. For this reason, maximum effort is needed to build innovation and commercialization of supremacy, perception (public relations) and scalability strategies so that internal operations can be reliable in achieving the vision of Sunan Ampel Islamic State University Surabaya.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>
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Krishna Chaitanya, G., Dinesh Reddy Meka, Vakalapudi Surya Vamsi, and M. V S Ravi Karthik. "A Survey on Twitter Sentimental Analysis with Machine Learning Techniques." International Journal of Engineering & Technology 7, no. 2.32 (2018): 462. http://dx.doi.org/10.14419/ijet.v7i2.32.16268.
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Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general.
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Pandey, Avinash Chandra, and Dharmveer Singh Rajpoot. "Improving Sentiment Analysis using Hybrid Deep Learning Model." Recent Advances in Computer Science and Communications 13, no. 4 (2020): 627–40. http://dx.doi.org/10.2174/2213275912666190328200012.
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Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.
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Seth, Pranav, Apoorv Sharma, and R. Vidhya. "Sentiment Analysis of Tweets Using Hadoop." International Journal of Engineering & Technology 7, no. 3.12 (2018): 434. http://dx.doi.org/10.14419/ijet.v7i3.12.16123.
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Blogging and networking platforms like Facebook, Reddit, Twitter and LinkedIn are social media channels where users can share their thoughts and opinions. Since online chatter is a vital and exhaustive source of information, these thoughts and opinions hold the key to the success of any endeavour. Tweets which are posted by millions all over the world can be used to analyse consumers’ opinions about individual products, services and campaigns. These tweets have proven to be a valuable source of information in the recent years, playing key roles in success of brands, businesses and politicians. We have tackled Sentiment Analysis with a lexicon-based approach for extracting positive, negative, and neutral tweets by using part-of-speech tagging from natural language processing. The approach manifests in the design of a software toolkit that facilitates the sentiment analysis. We collect dataset, i.e. the tweets are fetched from Twitter and text mining techniques like tokenization are executed to use it for building classifier that is able to predict sentiments for each tweet.
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Kim, Yoosin, Rahul Dwivedi, Jie Zhang, and Seung Ryul Jeong. "Competitive intelligence in social media Twitter: iPhone 6 vs. Galaxy S5." Online Information Review 40, no. 1 (2016): 42–61. http://dx.doi.org/10.1108/oir-03-2015-0068.
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Purpose – The purpose of this paper is to mine competitive intelligence in social media to find the market insight by comparing consumer opinions and sales performance of a business and one of its competitors by analyzing the public social media data. Design/methodology/approach – An exploratory test using a multiple case study approach was used to compare two competing smartphone manufacturers. Opinion mining and sentiment analysis are conducted first, followed by further validation of results using statistical analysis. A total of 229,948 tweets mentioning the iPhone6 or the GalaxyS5 have been collected for four months following the release of the iPhone6; these have been analyzed using natural language processing, lexicon-based sentiment analysis, and purchase intention classification. Findings – The analysis showed that social media data contain competitive intelligence. The volume of tweets revealed a significant gap between the market leader and one follower; the purchase intention data also reflected this gap, but to a less pronounced extent. In addition, the authors assessed whether social opinion could explain the sales performance gap between the competitors, and found that the social opinion gap was similar to the shipment gap. Research limitations/implications – This study compared the social media opinion and the shipment gap between two rival smart phones. A business can take the consumers’ opinions toward not only its own product but also toward the product of competitors through social media analytics. Furthermore, the business can predict market sales performance and estimate the gap with competing products. As a result, decision makers can adjust the market strategy rapidly and compensate the weakness contrasting with the rivals as well. Originality/value – This paper’s main contribution is to demonstrat the competitive intelligence via the consumer opinion mining of social media data. Researchers, business analysts, and practitioners can adopt this method of social media analysis to achieve their objectives and to implement practical procedures for data collection, spam elimination, machine learning classification, sentiment analysis, feature categorization, and result visualization.
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Rani, Meesala Shobha, and Sumathy S. "Perspectives of the performance metrics in lexicon and hybrid based approaches: a review." International Journal of Engineering & Technology 6, no. 4 (2017): 108. http://dx.doi.org/10.14419/ijet.v6i4.8295.
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Online social media and social networking services experience a drastic development in the present scenario. Contents generated by hundreds of millions of users are used for communication in general. Users mark their opinion and review in various applications such as Twitter, Facebook, YouTube, Weibo, Flicker, LinkedIn, Online-e commerce sites, Microblogging sites, etc. User generated text is spread rapidly on the web, and it has become tedious to analyze the opinionated text in order to arrive at a decision. Sentiment analysis, a sub-category of text mining is the major active research domain in current era due to greater quantity of opinionated text present in the Internet. Semantic detection is the sub-class in the sentiment analysis which is used for measuring the sentiment orientation in any text. Opinionated text is used for analyzing and making the decision simple. This interdisciplinary field draws various techniques from data mining, machine learning, natural language processing, lexicon based and hybrid based approaches. This paper provides a broad perspective with the highlight of the current state-of art techniques emphasizing the various research challenges and gaps present. The performance metrics in terms of detection rate, precision, recall, f-measure/score, average mean, auto-Pearson correlation, cosine similarity and ratio of time on various algorithms is discussed in detail. An analysis of the text mining approaches in different domains is presented.
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Mostafa, Golam, Ikhtiar Ahmed, and Masum Shah Junayed. "Investigation of Different Machine Learning Algorithms to Determine Human Sentiment Using Twitter Data." International Journal of Information Technology and Computer Science 13, no. 2 (2021): 38–48. http://dx.doi.org/10.5815/ijitcs.2021.02.04.
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In recent years, with the advancement of the internet, social media is a promising platform to explore what going on around the world, sharing opinions and personal development. Now, Sentiment analysis, also known as text mining is widely used in the data science sector. It is an analysis of textual data that describes subjective information available in the source and allows an rganization to identify the thoughts and feelings of their brand or goods or services while monitoring conversations and reviews online. Sentiment analysis of Twitter data is a very popular research work nowadays. Twitter is that kind of social media where many users express their opinion and feelings through small tweets and different machine learning classifier algorithms can be used to analyze those tweets. In this paper, some selected machine learning classifier algorithms were applied on crawled Twitter data after applying different types of preprocessors and encoding techniques, which ended up with satisfying accuracy. Later a comparison between the achieved accuracies was showed. Experimental evaluations show that the Neural Network Classifier’algorithm provides a remarkable accuracy of 81.33% compared with other classifiers.
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Utama, Heru Sukma, Didi Rosiyadi, Bobby Suryo Prakoso, and Dedi Ariadarma. "Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 3, no. 2 (2019): 243–50. http://dx.doi.org/10.29207/resti.v3i2.1050.
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Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Support Vector Machine Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Support Vector Machine algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying SVM Algorithm model. The results obtained from the study using the SVM model are obtained Confusion Matrix result, namely accuracyof 78.18%, Precision of 74.03%, and Sensitivity or Recall of 86.82%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.
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Utama, Heru Sukma, Didi Rosiyadi, Dedi Aridarma, and Bobby Suryo Prakoso. "SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN." Jurnal Pilar Nusa Mandiri 15, no. 2 (2019): 247–54. http://dx.doi.org/10.33480/pilar.v15i2.705.
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Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.
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Agustina, Dyah Auliya, Sri Subanti, and Etik Zukhronah. "Implementasi Text Mining Pada Analisis Sentimen Pengguna Twitter Terhadap Marketplace di Indonesia Menggunakan Algoritma Support Vector Machine." Indonesian Journal of Applied Statistics 3, no. 2 (2021): 109. http://dx.doi.org/10.13057/ijas.v3i2.44337.
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<p>In this digital era, technology development has changed the behavior of society from buy offline to online. One of this behavioral changes is marked by the growth of global marketplace including in Indonesia. The big marketplaces in Indonesia that have received a lot of public response on social media are Tokopedia, Shopee, and Bukalapak. This research determines the public sentiment toward both the service and issues surrounding these three marketplaces on media social especially Twitter. Public opinion is classified into a positive or negative sentiment. The data used in this study is obtained from Twitter API (Application Programming Interface) using keyword Shopee, Tokopedia, and Bukalapak. Preprocessing texts are divided into five steps: cleansing, case folding, stemming, stopwords, and tokenizing. Training and testing data are divided using <em>k</em>-fold cross validation method, while visualization the characteristic of text is using word cloud. Research shows that public are posting tweet more positive sentiment than negative one. The perfomance of classification shows that the best <em>G</em>-mean and AUC value for Bukalapak testing data are 0.85 and 0.86 in the first fold. While the best <em>G</em>-mean and AUC value for Shopee testing data are 0.76 and 0.77 in the seventh fold and the best <em>G</em>-mean and AUC value for Tokopedia testing data are 0.82 and 0.83 in the sixth fold.</p><p><strong>Keywords</strong> : sentiment analysis, marketplace, support vector machine, twitter</p>
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Putra, Berlian Juliartha Martin, Afrida Helen, and Ali Ridho Barakbah. "Rule-based Sentiment Degree Measurement of Opinion Mining of Community Participatory in the Government of Surabaya." EMITTER International Journal of Engineering Technology 6, no. 2 (2018): 200–216. http://dx.doi.org/10.24003/emitter.v6i2.275.
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Diskominfo Surabaya, as a government agency, received much community participatory for improvement of governmental services, with increasing number of 698, 2717, 4176 and 4298 participatory data respectively in 2011, 2012, 2013 and 2014. It is challenging for Diskominfo Surabaya to set a target by giving the response back within 24 hours. Due to task complexity to address the degree of participatory and to categorize the group of participatory, they faced difficulty to fulfill the target. In this research, we present a new system for measuring the sentiment degree of community participatory. We provide 5 functions in our system, which are: (1) Data Collection, (2) Data Preprocessing, (3) Text Mining, (4) Sentiment Analysis and (5) Validation. We propose our rule-based technique for the sentiment analysis of opinion mining with detection of 8 important parts, which are (1) Verb, (2) Adjective, (3) Preposition, (4) Noun, (5) Adverb, (6) Symbol, (7) Phrase, and (8) Complimentary. For applicability of our proposed system, we made a series of experiment with 410 data of community participatory in Twitter for Diskominfo Surabaya and compared with other sentiment classification algorithms which are SVM and Naive Bayes Classifier. Our system performed 77.32% rate of accuracy and outperformed to other comparing algorithms.
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Park, Seunghyun Brian, Jichul Jang, and Chihyung Michael Ok. "Analyzing Twitter to explore perceptions of Asian restaurants." Journal of Hospitality and Tourism Technology 7, no. 4 (2016): 405–22. http://dx.doi.org/10.1108/jhtt-08-2016-0042.
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Purpose The purpose of this paper is to use Twitter analysis to explore diner perceptions of four types of Asian restaurants (Chinese, Japanese, Korean and Thai). Design/methodology/approach Using 86,015 tweets referring to Asian restaurants, this research used text mining and sentiment analysis to find meaningful patterns, popular words and emotional states in opinions. Findings Twitter users held mingled perceptions of different types of Asian restaurants. Sentiment analysis and ANOVA showed that the average sentiment scores for Chinese restaurants was significantly lower than the other three Asian restaurants. While most positive tweets referred to food quality, many negative tweets suggested problems associated with service quality or food culture. Research limitations/implications This research provides a methodology that future researchers can use in applying social media analytics to explore major issues and extract sentiment information from text messages. Originality/value Limited research has been conducted applying social media analysis in hospitality research. This study fills a gap by using social media analytics with Twitter data to examine the Twitter users’ thoughts and emotions for four different types of Asian restaurants.
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Romadoni, Fajar, Yuyun Umaidah, and Betha Nurina Sari. "Text Mining Untuk Analisis Sentimen Pelanggan Terhadap Layanan Uang Elektronik Menggunakan Algoritma Support Vector Machine." Jurnal Sisfokom (Sistem Informasi dan Komputer) 9, no. 2 (2020): 247. http://dx.doi.org/10.32736/sisfokom.v9i2.903.
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Electronic money is a cashless payment instrument whose money is stored in media server or chip that can be moved for the benefit of payment transactions or fund transfers. In Indonesia, there are already many electronic money products, one of which is OVO. OVO is very popular with the people of Indonesia because it offers many promos such as discounts and cashback. But over time, that much promotion is detrimental to OVO shareholders, so the portion of promo given by OVO to its customers is finally reduced. That incident caused many pros and cons opinions about OVO, one of them is on social media Twitter. Sentiment analysis can be used as a solution to process the opinions of OVO customers on Twitter. This study aims to classify the customer opinions on OVO services into positive and negative classes. This study uses the Support Vector Machine algorithm with 3852 data taken from Twitter with keyword @ovo_id using web scraping techniques. The dataset divided into two classes, 2034 positive and 1818 negative sentiment data. The classification process is carried out with four splitting data scenarios, with 60:40, 70:30, 80:20, 90:10 data ratio and with four kernel such as linear, rbf, sigomid, and polynomial. The final results show that the greatest accuracy value obtained by linear kernel with 90:10 data ratio which gets an accuracy value of 98.7%.
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Monicka, M. B., and A. Krishnaveni. "Sentiment Analysis on Myocardial Infarction Using Tweets Data." Asian Journal of Computer Science and Technology 8, S1 (2019): 10–14. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1987.
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In 2016, the survey reports that 1.7 Million people die of Myocardial Infarction (MI), due to less medication facilities, less prevention care and treatment planning is top most analysis of effective disease risk assessment, through this we have take prevention using sentiment analysis of recent advancements, the text analytics have opened up new potential of using the rich information of tweet analysis, to identify the relevant risk factors in MI. To tackle the MI risk factors tweet analysis gives more remedy and care factors by users, also this leads to decrease of MI in India. Our system plays a machine learning approach using sentiment analysis using tweet dataset. Nowadays people suffering from MI such as cardiac arrest, high blood pressure, congestive heart failure etc. Twitter is an excellent resource for the MI Patients since they connect people who have with similar conditions and experiences. It provides the knowledge sharing about MI, plays a vital role through Opinion Mining system.
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Nofiyanti, Endah, and Erry Maricha Oki Nur Haryanto. "Analisis Sentimen terhadap Penanggulangan Bencana di Indonesia." Jurnal Ilmiah SINUS 19, no. 2 (2021): 17. http://dx.doi.org/10.30646/sinus.v19i2.563.
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Disasters have become a part of human life, whether natural disaster, non-natural disaster or from human error, which is causes fatalities, environmental damages, property losses, or psychological impact, especially in Indonesia. The National Agency for Disaster Countermeasure (BNPB) is the Indonesian board for natural disaster affairs. For each region, Badan Penanggulangan Bencana Daerah (BPBD) as for as the regional disaster management. Social media has become a part of everyday life for people nowdays. The purpose of this research is to find out the public reaction, with classification positive, neutral or negative opinion to the disaster management in Indonesia from Twitter. One text mining method from Natural Processing Language (NLP) is sentiment analysis. Sentiment analysis applied to analyze data with public opinion as the decision-making support. Based on the research, there were 23,53 % positive tweets, 57,35 % neutral tweets and 19,12 % negative tweets. From the result, mostly Indonesian have neutral opinion about the disaster management. The result also displayed in histogram, pie chart and word cloud.
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Aliyah, Salma Farah, Hasbi Yasin, Suparti Suparti, Budi Warsito, and Tatik Widiharih. "ANALISIS SENTIMEN PT TIKI JALUR NUGRAHA EKAKURIR (PT TIKI JNE) PADA MEDIA SOSIAL TWITTER MENGGUNAKAN MODEL FEED FORWARD NEURAL NETWORK." Jurnal Statistika Universitas Muhammadiyah Semarang 8, no. 2 (2020): 103. http://dx.doi.org/10.26714/jsunimus.8.2.2020.103-113.
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In the 2000s until now, e-commerce systems have continued to develop throughout the world and even in Indonesia. PT Tiki Jalur Nugraha Ekakurir (PT Tiki JNE) is a freight forwarding company that provides convenience for the public in carrying out online shopping activities, and shipping other goods. The large volume of shipments makes PT Tiki JNE have several problems in service that have led to several kinds of responses from users. Sentiment analysis on Twitter social media can be an option to see how PT Tiki JNE’s users respond to services that have been provided. These responses are classified into positive sentiments and negative sentiments. In this research data processing is performed using text mining as the initial source of numerical data from document data which will later be classified using the Artificial Neural Network model with the Resilient Backpropagation algorithm. Data labeling is done manually and sentiment scoring. The test results show that the best model obtained is FFNN 867-7-1 by using the evaluation model 10-Fold Cross Validation to get an overall accuracy performance of 80.27%, kappa accuracy of 39.13%, precision of 69.04%, recall of 70.56%, and f-measure of 69.8% which can be interpreted that the model used is quite good. Analysis of the results using wordcloud shows the tendency of opinion sentiment categories depending on the words used in the tweet.
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Dan, Flaviu Bogdan, Monica Maer-Matei, and Stelian Stancu. "Use of Social Networks in Determining stockmarket Evolution." Proceedings of the International Conference on Applied Statistics 2, no. 1 (2020): 126–38. http://dx.doi.org/10.2478/icas-2021-0012.
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Abstract This article aims to use text mining methods and sentiment analysis to determine the stock market evolution of companies as well as virtual currencies such as Bitcoin. The source of the text is the social media channel Twitter and the text is composed of individual messages sent by users. Although previous papers proved with a degree of certainty that this paper hypothesis is true, as we will see bellow, the area of research was focused only on the professional environment or known opinion makers and not taking into account a high population mass. To ensure that a high level of information is maintained after the sentiment analysis process, we will use multiple algorithms based on different calculation methods and different word dictionaries. In addition, indicators such as the number of assessments, the number of replays etc. will be added to the methodology. By the end of the paper we will be able to both identify a working methodology of analyzing text for the purposes of stock market prediction and also we will touch on the limitations faced when creating it and the ways through which we can expand and improve it’s reliability. The implementation of all these methods and of the multiple dictionaries helped us in simulating human behavior and the differences of opinion, when a group wants to analyze a text. The algorithm becoming a way to balance the different “opinions” that resulted out of the sentiment analysis.
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Neelakandan, S., and D. Paulraj. "A gradient boosted decision tree-based sentiment classification of twitter data." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 04 (2020): 2050027. http://dx.doi.org/10.1142/s0219691320500277.
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People communicate their views, arguments and emotions about their everyday life on social media (SM) platforms (e.g. Twitter and Facebook). Twitter stands as an international micro-blogging service that features a brief message called tweets. Freestyle writing, incorrect grammar, typographical errors and abbreviations are some noises that occur in the text. Sentiment analysis (SA) centered on a tweet posted by the user, and also opinion mining (OM) of the customers review is another famous research topic. The texts are gathered from users’ tweets by means of OM and automatic-SA centered on ternary classifications, namely positive, neutral and negative. It is very challenging for the researchers to ascertain sentiments as a result of its limited size, misspells, unstructured nature, abbreviations and slangs for Twitter data. This paper, with the aid of the Gradient Boosted Decision Tree classifier (GBDT), proposes an efficient SA and Sentiment Classification (SC) of Twitter data. Initially, the twitter data undergoes pre-processing. Next, the pre-processed data is processed using HDFS MapReduce. Now, the features are extracted from the processed data, and then efficient features are selected using the Improved Elephant Herd Optimization (I-EHO) technique. Now, score values are calculated for each of those chosen features and given to the classifier. At last, the GBDT classifier classifies the data as negative, positive, or neutral. Experiential results are analyzed and contrasted with the other conventional techniques to show the highest performance of the proposed method.
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Sugiyarto, Sugiyarto, Joko Eliyanto, Nursyiva Irsalinda, Zhurwahayati Putri, and Meita Fitrianawat. "A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN." JTAM (Jurnal Teori dan Aplikasi Matematika) 5, no. 1 (2021): 110. http://dx.doi.org/10.31764/jtam.v5i1.3766.
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Sentiment analysis is an analysis with an objective to identify like, dislike, comments, opinion, or feedback on certain content which will be categorized into positive, negative, or neutral. In general selection, sentiment analysis widely known to be used to predict the winner on election process. This method tries to dig the people sentiment on their governor candidates during election, whether it’s positive, negative, or neutral opinion. The output of the positive sentiment is related to people acceptance towards one of the election nominee. That statement usually applied as a base reference for determining the result of the election process. In sentiment analysis, the importance of its fuzzy logics must be considered. Each of the people statement is assumed to have the level of positive, negative, or neutral percentage. The concept of fuzzy logic is developed and applied on one of this text mining method. This research is focusing on comparison analysis and fuzzy logic application in sentiment analysis method. Two method which discussed in this research are Fuzzy Naïve Bayes and Sentiment Fuzzy with convolutional neural network. This research is applied on PILKADA of Solo and Medan district case study. The data of the people opinion are acquired from twitter and collected on September 2020 to December 2020. The two methods which mentioned before are implemented on the acquired data and the output of these method application then compared. The conclusion of this research suggest that different approach will resulting in different output.
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Karami, Amir, Vishal Shah, Reza Vaezi, and Amit Bansal. "Twitter speaks: A case of national disaster situational awareness." Journal of Information Science 46, no. 3 (2019): 313–24. http://dx.doi.org/10.1177/0165551519828620.
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In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental and human losses. The unpredictable nature of natural disasters behaviour makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyse public concerns during natural disasters; however, this approach is limited, expensive and time-consuming. Luckily, the advent of social media has provided scholars with an alternative means of analysing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasises the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA). This framework uses text mining methods including sentiment analysis and topic modelling to create a better SA for disaster preparedness, response and recovery. TwiSA has also effectively deployed on a large number of tweets and tracks the negative concerns of people during the 2015 South Carolina flood.
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Alothman, Manal Othman, Muhammad Badruddin Khan, and Mozaherul Hoque Abul Hasanat. "Review of Researches on Arabic Social Media Text Mining." Journal of Intelligent Systems and Computing 2, no. 1 (2021): 20–33. http://dx.doi.org/10.51682/jiscom.00201005.2021.
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Social media sites and applications have allowed people to share their comments, opinions, and point of views in different languages on mass scale. Arabic language is one of the languages that has seen huge surge in production of its digital textual content. The Arabic content and its metadata are a goldmine of useful information for a wide variety of applications. A large number of researchers are working on Arabic data in various domains of research such as natural language processing, sentiment analysis, event detection, named entity recognition, etc. This article presents a review of number of such studies conducted between 2014 and 2019 using their data sources from social media websites. We found that Twitter was the most used source to contribute data for dataset construction for Arabic text mining researchers. Our study also found that the Sport Vector Machine (SVM) and Naïve Bayesian (NB) classifiers were the most used classifiers in the previous researches. Moreover, the results of the previous studies indicate that SVM classifier provided the best performance compared to other classifiers.
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Chintalapudi, Nalini, Gopi Battineni, and Francesco Amenta. "Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models." Infectious Disease Reports 13, no. 2 (2021): 329–39. http://dx.doi.org/10.3390/idr13020032.
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The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading false news on social media sites like Twitter is creating unnecessary anxiety towards this disease. The motto behind this study is to analyses tweets by Indian netizens during the COVID-19 lockdown. The data included tweets collected on the dates between 23 March 2020 and 15 July 2020 and the text has been labelled as fear, sad, anger, and joy. Data analysis was conducted by Bidirectional Encoder Representations from Transformers (BERT) model, which is a new deep-learning model for text analysis and performance and was compared with three other models such as logistic regression (LR), support vector machines (SVM), and long-short term memory (LSTM). Accuracy for every sentiment was separately calculated. The BERT model produced 89% accuracy and the other three models produced 75%, 74.75%, and 65%, respectively. Each sentiment classification has accuracy ranging from 75.88–87.33% with a median accuracy of 79.34%, which is a relatively considerable value in text mining algorithms. Our findings present the high prevalence of keywords and associated terms among Indian tweets during COVID-19. Further, this work clarifies public opinion on pandemics and lead public health authorities for a better society.
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Abdulla, Salam, and Mzhda Hiwa Hama. "Sentiment Analyses for Kurdish Social Network Texts using Naive Bayes Classifier." Journal of University of Human Development 1, no. 4 (2015): 393. http://dx.doi.org/10.21928/juhd.v1n4y2015.pp393-397.
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Language is a great tool to communicate and carry information. Moreover, it is used to express feeling and sentiment. These days sentiment analysis is one the most active field of research, to discover people's opinion about specific product, service or topic. The task of sentiment classification is to categories reviews of users as positive or negative from textual information of Social Networks like Facebook, Google+, Twitter and Blogs to determine the feeling of majority about specific topics. Kurdish language suffer from the unique and standard writing rules, grammar syntax and alphabet. Therefore, Kurdish people write their feeling in social networks in different ways. Some of them prefer to use the Arabic script style while others prefer to use Latin letters to express their feeling, further some people use their different accents and syntax and even sometimes they use English letters write their emotion. Therefore, for the purpose of analytics for Kurdish sentiment analyses its proposed to use data mining classification techniques such as Naive Bayes classifier because of its strong independence assumption. In Experimental results, the Social Network comments are classified into positive or negative polarities. The accuracy of sentiment analysis is obtained 66% by using Naive Bayes classifier for unigram feature on Kurdish text dataset.
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Ciasullo, Maria Vincenza, Orlando Troisi, Francesca Loia, and Gennaro Maione. "Carpooling: travelers’ perceptions from a big data analysis." TQM Journal 30, no. 5 (2018): 554–71. http://dx.doi.org/10.1108/tqm-11-2017-0156.
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Purpose The purpose of this paper is to provide a better understanding of the reasons why people use or do not use carpooling. A further aim is to collect and analyze empirical evidence concerning the advantages and disadvantages of carpooling. Design/methodology/approach A large-scale text analytics study has been conducted: the collection of the peoples’ opinions have been realized on Twitter by means of a dedicated web crawler, named “Twitter4J.” After their mining, the collected data have been treated through a sentiment analysis realized by means of “SentiWordNet.” Findings The big data analysis identified the 12 most frequently used concepts about carpooling by Twitter’s users: seven advantages (economic efficiency, environmental efficiency, comfort, traffic, socialization, reliability, curiosity) and five disadvantages (lack of effectiveness, lack of flexibility, lack of privacy, danger, lack of trust). Research limitations/implications Although the sample is particularly large (10 percent of the data flow published on Twitter from all over the world in about one year), the automated collection of people’s comments has prevented a more in-depth analysis of users’ thoughts and opinions. Practical implications The research findings may direct entrepreneurs, managers and policy makers to understand the variables to be leveraged and the actions to be taken to take advantage of the potential benefits that carpooling offers. Originality/value The work has utilized skills from three different areas, i.e., business management, computing science and statistics, which have been synergistically integrated for customizing, implementing and using two IT tools capable of automatically identifying, selecting, collecting, categorizing and analyzing people’s tweets about carpooling.
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Hao, Jianqiang, and Hongying Dai. "Social media content and sentiment analysis on consumer security breaches." Journal of Financial Crime 23, no. 4 (2016): 855–69. http://dx.doi.org/10.1108/jfc-01-2016-0001.
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Purpose Security breaches have been arising issues that cast a large amount of financial losses and social problems to society and people. Little is known about how social media could be used a surveillance tool to track messages related to security breaches. This paper aims to fill the gap by proposing a framework in studying the social media surveillance on security breaches along with an empirical study to shed light on public attitudes and concerns. Design/methodology/approach In this study, the authors propose a framework for real-time monitoring of public perception to security breach events using social media metadata. Then, an empirical study was conducted on a sample of 1,13,340 related tweets collected in August 2015 on Twitter. By text mining a large number of unstructured, real-time information, the authors extracted topics, opinions and knowledge about security breaches from the general public. The time series analysis suggests significant trends for multiple topics and the results from sentiment analysis show a significant difference among topics. Findings The study confirms that social media monitoring provides a supplementary tool for the traditional surveys which are costly and time-consuming to track security breaches. Sentiment score and impact factors are good predictors of real-time public opinions and attitudes to security breaches. Unusual patterns/events of security breaches can be detected in the early stage, which could prevent further destruction by raising public awareness. Research limitations/implications The sample data were collected from a short period of time on Twitter. Future study could extend the research to a longer period of time or expand key words search to observe the sentiment trend, especially before and after large security breaches, and to track various topics across time. Practical implications The findings could be useful to inform public policy and guide companies responding to consumer security breaches in shaping public perception. Originality/value This study is the first of its kind to undertake the analysis of social media (Twitter) content and sentiment on public perception to security breaches.
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Kusal, Sheetal, Shruti Patil, Ketan Kotecha, Rajanikanth Aluvalu, and Vijayakumar Varadarajan. "AI Based Emotion Detection for Textual Big Data: Techniques and Contribution." Big Data and Cognitive Computing 5, no. 3 (2021): 43. http://dx.doi.org/10.3390/bdcc5030043.
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Online Social Media (OSM) like Facebook and Twitter has emerged as a powerful tool to express via text people’s opinions and feelings about the current surrounding events. Understanding the emotions at the fine-grained level of these expressed thoughts is important for system improvement. Such crucial insights cannot be completely obtained by doing AI-based big data sentiment analysis; hence, text-based emotion detection using AI in social media big data has become an upcoming area of Natural Language Processing research. It can be used in various fields such as understanding expressed emotions, human–computer interaction, data mining, online education, recommendation systems, and psychology. Even though the research work is ongoing in this domain, it still lacks a formal study that can give a qualitative (techniques used) and quantitative (contributions) literature overview. This study has considered 827 Scopus and 83 Web of Science research papers from the years 2005–2020 for the analysis. The qualitative review represents different emotion models, datasets, algorithms, and application domains of text-based emotion detection. The quantitative bibliometric review of contributions presents research details such as publications, volume, co-authorship networks, citation analysis, and demographic research distribution. In the end, challenges and probable solutions are showcased, which can provide future research directions in this area.
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Oyebode, Oladapo, Chinenye Ndulue, Ashfaq Adib, et al. "Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach." JMIR Medical Informatics 9, no. 4 (2021): e22734. http://dx.doi.org/10.2196/22734.
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Background The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. Objective This paper aims to investigate the impact of the COVID-19 pandemic on people worldwide using social media data. Methods We applied natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collected over 47 million COVID-19–related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes. Results A total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. Conclusions We uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics.
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Gangawane, Aarti, and H. B. "Opinion Mining and Sentiment Analysis on Twitter." International Journal of Computer Applications 182, no. 10 (2018): 32–35. http://dx.doi.org/10.5120/ijca2018917718.
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Sharma, Yashvardhan, Ekansh Mittal, and Mayank Garg. "Political Opinion Mining from Twitter." International Journal of Information Systems in the Service Sector 8, no. 4 (2016): 47–56. http://dx.doi.org/10.4018/ijisss.2016100104.
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Twitter is one of the most popular micro-blogging platform for people to express their political views in and around the elections. Hence during pre-elections twitter becomes a rich resource of data to understand the changing tenor of political leaders with time. During this time, when views, opinions and judgments are shared so prolifically through online media, tools which can provide the crux of this content are paramount. In this paper the authors have developed one such sentiment analysis tool to analyze the changing political views of persons with time. Using the tool they classify the tweets as positive, negative or neutral and studying it over time the authors successfully estimate the mood of the person. The authors have also developed a specialized phonetic dictionary to provide better approximation for most commonly used slangs and abbreviations.
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Patodkar, Vaibhavi N., and Sheikh I.R. "Twitter as a Corpus for Sentiment Analysis and Opinion Mining." IJARCCE 5, no. 12 (2016): 320–22. http://dx.doi.org/10.17148/ijarcce.2016.51274.
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