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Journal articles on the topic 'Twitter sentiment polarity classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Risnantoyo, Ricky, Arifin Nugroho, and Kresna Mandara. "Sentiment Analysis on Corona Virus Pandemic Using Machine Learning Algorithm." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 4, no. 1 (2020): 86–96. http://dx.doi.org/10.31289/jite.v4i1.3798.

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Corona virus outbreaks that occur in almost all countries in the world have an impact not only in the health sector, but also in other sectors such as tourism, finance, transportation, etc. This raises a variety of sentiments from the public with the emergence of corona virus as a trending topic on Twitter social media. Twitter was chosen by the public because it can disseminate information in real time and can see market reactions quickly. This research uses "tweet" data or public tweet related to "Corona Virus" to see how the sentiment polarity arises. Text mining techniques and three machin

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Abu Bakar, Normi Sham Awang, Ros Aziehan Rahmat, and Umar Faruq Othman. "Polarity Classification Tool for Sentiment Analysis in Malay Language." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 3 (2019): 259. http://dx.doi.org/10.11591/ijai.v8.i3.pp259-263.

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<p>The popularity of the social media channels has increased the interest among researchers in the sentiment analysis(SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool(MaCT). This tool is developed based on the AFINN sent

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Al-Kabi, Mohammed N., Heider A. Wahsheh, and Izzat M. Alsmadi. "Polarity Classification of Arabic Sentiments." International Journal of Information Technology and Web Engineering 11, no. 3 (2016): 32–49. http://dx.doi.org/10.4018/ijitwe.2016070103.

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Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DA

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Montejo-Ráez, Arturo, Eugenio Martínez-Cámara, M. Teresa Martín-Valdivia, and L. Alfonso Ureña-López. "Ranked WordNet graph for Sentiment Polarity Classification in Twitter." Computer Speech & Language 28, no. 1 (2014): 93–107. http://dx.doi.org/10.1016/j.csl.2013.04.001.

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Mahajan, Prerna, and Anamika Rana. "Sentiment Classification-How to Quantify Public Emotions Using Twitter." International Journal of Sociotechnology and Knowledge Development 10, no. 1 (2018): 57–71. http://dx.doi.org/10.4018/ijskd.2018010104.

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This article describes how with the tremendous popularity in the usage of social media has led to the explosive growth in unstructured data available on various social networking sites. Sentiment analysis of textual data collected from such platforms has become an important research area. In this article, the sentiment classification approach which employs an emotion detection technique is presented. To identify the emotions this paper uses the NRC lexicon based approach for identifying polarity of emotions. A score is computed to quantify emotions obtained from NRC lexicon approach. The metho

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Lohar, Pintu, Haithem Afli, and Andy Way. "Maintaining Sentiment Polarity in Translation of User-Generated Content." Prague Bulletin of Mathematical Linguistics 108, no. 1 (2017): 73–84. http://dx.doi.org/10.1515/pralin-2017-0010.

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Abstract The advent of social media has shaken the very foundations of how we share information, with Twitter, Facebook, and Linkedin among many well-known social networking platforms that facilitate information generation and distribution. However, the maximum 140-character restriction in Twitter encourages users to (sometimes deliberately) write somewhat informally in most cases. As a result, machine translation (MT) of user-generated content (UGC) becomes much more difficult for such noisy texts. In addition to translation quality being affected, this phenomenon may also negatively impact s

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Hiriyannaiah, Srinidhi, G. M. Siddesh, and K. G. Srinivasa. "Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis." International Journal of Information Technology and Web Engineering 13, no. 3 (2018): 99–111. http://dx.doi.org/10.4018/ijitwe.2018070107.

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This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analys

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Wagh, Bhagyashri, J. V. Shinde, and P. A. Kale. "A Twitter Sentiment Analysis Using NLTK and Machine Learning Techniques." International Journal of Emerging Research in Management and Technology 6, no. 12 (2018): 37. http://dx.doi.org/10.23956/ijermt.v6i12.32.

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In today’s world, Social Networking website like Twitter, Facebook , Tumbler, etc. plays a very significant role. Twitter is a micro-blogging platform which provides a tremendous amount of data which can be used for various application of sentiment Analysis like predictions, review, elections, marketing, etc Sentiment Analysis is a process of extracting information from large amount of data, and classifies them into different classes called sentiments. Python is simple yet powerful, high-level, interpreted and dynamic programming language, which is well known for its functionality of processin

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Shofiya, Carol, and Samina Abidi. "Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data." International Journal of Environmental Research and Public Health 18, no. 11 (2021): 5993. http://dx.doi.org/10.3390/ijerph18115993.

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Background: COVID-19 preventive measures have been an obstacle to millions of people around the world, influencing not only their normal day-to-day activities but also affecting their mental health. Social distancing is one such preventive measure. People express their opinions freely through social media platforms like Twitter, which can be shared among other users. The articulated texts from Twitter can be analyzed to find the sentiments of the public concerning social distancing. Objective: To understand and analyze public sentiments towards social distancing as articulated in Twitter textu

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Xing, Yongping, Chuangbai Xiao, Yifei Wu, and Ziming Ding. "A Convolutional Neural Network for Aspect-Level Sentiment Classification." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 14 (2019): 1959046. http://dx.doi.org/10.1142/s0218001419590468.

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Sentiment analysis, including aspect-level sentiment classification, is an important basic natural language processing (NLP) task. Aspect-level sentiment can provide complete and in-depth results. Words with different contexts variably influence the aspect-level sentiment polarity of sentences, and polarity varies based on different aspects of a sentence. Recurrent neural networks (RNNs) are regarded as effective models for handling NLP and have performed well in aspect-level sentiment classification. Extensive literature exists on sentiment classification that utilizes convolutional neural ne

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Gupta, Itisha, and Nisheeth Joshi. "Enhanced Twitter Sentiment Analysis Using Hybrid Approach and by Accounting Local Contextual Semantic." Journal of Intelligent Systems 29, no. 1 (2019): 1611–25. http://dx.doi.org/10.1515/jisys-2019-0106.

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Abstract This paper addresses the problem of Twitter sentiment analysis through a hybrid approach in which SentiWordNet (SWN)-based feature vector acts as input to the classification model Support Vector Machine. Our main focus is to handle lexical modifier negation during SWN score calculation for the improvement of classification performance. Thus, we present naive and novel shift approach in which negation acts as both sentiment-bearing word and modifier, and then we shift the score of words from SWN based on their contextual semantic, inferred from neighbouring words. Additionally, we augm

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Park, Hoyeon, Hyeonjeong Seo, Kyoung Jae Kim, and Gundoo Moon. "Application of Machine Learning Techniques to Tweet Polarity Classification with News Topic Analysis." International Journal of Engineering & Technology 7, no. 4.4 (2018): 40. http://dx.doi.org/10.14419/ijet.v7i4.4.19606.

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The exponential growth of online community provides the tremendous amount of textual information in terms of human behavioral reaction. Thus, online social media platforms such as Twitters, Facebook and YouTube are reflected as an essential part of human relationship networks. Especially, Twitter is widely applied to the disaster situation as a text and it provides critical insights into emergency management. In this study, we propose a topic analysis and sentiment polarity classification with machine learning techniques for emergency management. In this study, we compared the polarity classif

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Singh, Purva. "Covhindia: Deep Learning Framework for Sentiment Polarity Detection of Covid-19 Tweets in Hindi." International Journal on Natural Language Computing 9, no. 5 (2020): 23–34. http://dx.doi.org/10.5121/ijnlc.2020.9502.

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On 11th March 2020, the World Health Organization (WHO) declared Corona Virus Disease of 2019 (COVID-19) as a pandemic. Over time, the exponential growth of this disease has highlighted a mixture of sentiments expressed by the general population from various parts of the world speaking varied languages. It is, therefore, essential to analyze the public sentiment during this wave of the pandemic. While much work prevails to determine the sentiment polarity for tweets related to COVID-19, expressed in the English language, we still need to work on public sentiments expressed in languages other t

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Khalida, Rakhmi, and Siti Setiawati. "Analisis Sentimen Sistem E-Tilang Menggunakan Algoritma Naive Bayes Dengan Optimalisasi Information Gain." Journal of Informatic and Information Security 1, no. 1 (2020): 19–26. http://dx.doi.org/10.31599/jiforty.v1i1.137.

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Abstract  The Government of Indonesia took steps to change the system to improve public services in traffic violations by implementing the e-ticketing system. This system is a solution for disciplining motorized motorists from committing traffic violations. The existence of e-ticketing is also a solution to prevent the delinquency of law enforcers from illegal levies, peace terms in place, to accountability of fines. In this study, sentiment analysis of the e-ticketing system or opinion mining to classify the variety of public comments that give a positive, negative or neutral impr

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Tyagi, Abhilasha, and Naresh Sharma. "Sentiment Analysis using Logistic Regression and Effective Word Score Heuristic." International Journal of Engineering & Technology 7, no. 2.24 (2018): 20. http://dx.doi.org/10.14419/ijet.v7i2.24.11991.

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Sentiment Analysis is a method for judging somebody's sentiment or feeling with respect to a specific thing. It is utilized to recognize and arrange the sentiments communicated in writings. The web-based social networking sites like twitter draws in a huge number of clients that are online for imparting their insights in the form of tweets or comments. The tweets can be then classified into positive, negative, or neutral. In the proposed work, logistic regression classification is used as a classifier and unigram as a feature vector. For accuracy, k fold cross validation data mining technique

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Rahmawati, Siti, and Muhammad Habibi. "Public Sentiments Analysis about Indonesian Social Insurance Administration Organization on Twitter." IJID (International Journal on Informatics for Development) 9, no. 2 (2020): 87–93. http://dx.doi.org/10.14421/ijid.2020.09205.

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Insurance Administration Organization, which can be used by all people. However, this organization has received various criticisms from the public through social media, namely Twitter. This study aims to analyze public sentiment about the Indonesian Social Insurance Administration Organization on Twitter. The method used in this research is the Naive Bayes Classifier (NBC) method and uses the Support Vector Machine (SVM) method as a comparison. The amount of data used was 12,990 tweets with a data collection period from September 14, 2019 - February 18, 2020. The study compared the two classif

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Abdulsattar A. Jabbar Alkubaisi, Ghaith, Siti Sakira Kamaruddin, and Husniza Husni. "Conceptual Framework for Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naïve Bayes Classifiers." International Journal of Engineering & Technology 7, no. 2.14 (2018): 57. http://dx.doi.org/10.14419/ijet.v7i2.14.11156.

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Sentiment analysis has gained a lot of importance in last decade especially on the availability of data from Twitter that has created more interest for research in this field. Nevertheless, stock market classification models still suffer less accuracy and this has affected negatively the stock market indicators. In this paper, a new framework related to sentiment analysis from Twitter posts is proposed. The proposed framework represents an improved design of classification model that works to improve the classification accuracy to support decision makers in the domain of stock market exchange.

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AL-Jumaili, Ahmed Sabah. "A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis." Baghdad Science Journal 17, no. 1(Suppl.) (2020): 0385. http://dx.doi.org/10.21123/bsj.2020.17.1(suppl.).0385.

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Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniqu

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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 u

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Ravichandran, M., G. Kulanthaivel, and T. Chellatamilan. "Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest." Scientific World Journal 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/617358.

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Every day, huge numbers of instant tweets (messages) are published on Twitter as it is one of the massive social media for e-learners interactions. The options regarding various interesting topics to be studied are discussed among the learners and teachers through the capture of ideal sources in Twitter. The common sentiment behavior towards these topics is received through the massive number of instant messages about them. In this paper, rather than using the opinion polarity of each message relevant to the topic, authors focus on sentence level opinion classification upon using the unsupervi

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Mahendhiran, P. D., and S. Kannimuthu. "Deep Learning Techniques for Polarity Classification in Multimodal Sentiment Analysis." International Journal of Information Technology & Decision Making 17, no. 03 (2018): 883–910. http://dx.doi.org/10.1142/s0219622018500128.

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Contemporary research in Multimodal Sentiment Analysis (MSA) using deep learning is becoming popular in Natural Language Processing. Enormous amount of data are obtainable from social media such as Facebook, WhatsApp, YouTube, Twitter and microblogs every day. In order to deal with these large multimodal data, it is difficult to identify the relevant information from social media websites. Hence, there is a need to improve an intellectual MSA. Here, Deep Learning is used to improve the understanding and performance of MSA better. Deep Learning delivers automatic feature extraction and supports

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Simanjuntak, Tigor Nirman, and Setia Pramana. "Sentiment Analysis on Overseas Tweets on the Impact of COVID-19 in Indonesia." Indonesian Journal of Statistics and Its Applications 5, no. 2 (2021): 304–13. http://dx.doi.org/10.29244/ijsa.v5i2p304-313.

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This study aims to conduct analysis to determine the trend of sentiment on tweets about Covid-19 in Indonesia from the Twitter accounts overseas on big data perspective. The data was obtained from Twitter in the period of April 2020, with the word query "Indonesian Corona Virus" from foreign user accounts in English. The process of retrieving data comes from Twitter tweets by crawling the text using Twitter's API (Application Programming Interface) by employing Python programming language. Twitter was chosen because it is very fast and easy to spread through status updates from and among the u

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A.Jabbar Alkubaisi, Ghaith Abdulsattar, Siti Sakira Kamaruddin, and Husniza Husni. "Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers." Computer and Information Science 11, no. 1 (2018): 52. http://dx.doi.org/10.5539/cis.v11n1p52.

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Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accura

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Fatima-Zahrae, Sifi, Sabbar Wafae, and El Mzabi Amal. "Application of Latent Dirichlet Allocation (LDA) for clustering financial tweets." E3S Web of Conferences 297 (2021): 01071. http://dx.doi.org/10.1051/e3sconf/202129701071.

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Sentiment classification is one of the hottest research areas among the Natural Language Processing (NLP) topics. While it aims to detect sentiment polarity and classification of the given opinion, requires a large number of aspect extractions. However, extracting aspect takes human effort and long time. To reduce this, Latent Dirichlet Allocation (LDA) method have come out recently to deal with this issue.In this paper, an efficient preprocessing method for sentiment classification is presented and will be used for analyzing user’s comments on Twitter social network. For this purpose, differe

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Chung, Siyoung, Mark Chong, Jie Sheng Chua, and Jin Cheon Na. "Evolution of corporate reputation during an evolving controversy." Journal of Communication Management 23, no. 1 (2019): 52–71. http://dx.doi.org/10.1108/jcom-08-2018-0072.

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PurposeThe purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.Design/methodology/approachUsing a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and

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Khairunnisa, Syifa, Adiwijaya Adiwijaya, and Said Al Faraby. "Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19)." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 2 (2021): 406. http://dx.doi.org/10.30865/mib.v5i2.2835.

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COVID-19 is a pandemic that is troubling many people. This has led to a lot of public comments on Twitter social media. The comments are used for sentiment analysis so that we know the polarity of the sentiment that appears, whether it is positive, negative, or neutral. The problem when using twitter data is that the tweet data still contains many non-standard words such as abbreviated writing due to the maximum limitation of characters that can be used in one tweet. Preprocessing is the most important initial stage in sentiment analysis when using Twitter data, because it affects the classifi

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Zeng, Biqing, Heng Yang, Ruyang Xu, Wu Zhou, and Xuli Han. "LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification." Applied Sciences 9, no. 16 (2019): 3389. http://dx.doi.org/10.3390/app9163389.

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Aspect-based sentiment classification (ABSC) aims to predict sentiment polarities of different aspects within sentences or documents. Many previous studies have been conducted to solve this problem, but previous works fail to notice the correlation between the aspect’s sentiment polarity and the local context. In this paper, a Local Context Focus (LCF) mechanism is proposed for aspect-based sentiment classification based on Multi-head Self-Attention (MHSA). This mechanism is called LCF design, and utilizes the Context features Dynamic Mask (CDM) and Context Features Dynamic Weighted (CDW) laye

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Zhang, Ling, Wei Dong, and Xiangming Mu. "Analysing the features of negative sentiment tweets." Electronic Library 36, no. 5 (2018): 782–99. http://dx.doi.org/10.1108/el-05-2017-0120.

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Purpose This paper aims to address the challenge of analysing the features of negative sentiment tweets. The method adopted in this paper elucidates the classification of social network documents and paves the way for sentiment analysis of tweets in further research. Design/methodology/approach This study classifies negative tweets and analyses their features. Findings Through negative tweet content analysis, tweets are divided into ten topics. Many related words and negative words were found. Some indicators of negative word use could reflect the degree to which users release negative emotion

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Prianto, Cahyo, Nisa Hanum Harani, and Indra Firmansyah. "Analisis Sentimen Terhadap Kandidat Presiden Republik Indonesia Pada Pemilu 2019 di Media Sosial Twitter." JURNAL MEDIA INFORMATIKA BUDIDARMA 3, no. 4 (2019): 405. http://dx.doi.org/10.30865/mib.v3i4.1549.

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The development of technology today has been growing rapidly and has an impact on the behavior patterns of people who feel it. The Ministry of Communication and Information (KOMINFO) released a data that of 265 million people of Indonesia, there are around 54% have used internet technology or about 143 million people. In one survey IDN Research Institute said that there are three Social Media that are widely used in Indonesia, namely Facebook, Instagram and Twitter. This study focuses on extracting data in the form of text produced from social media twitter that responds to the account of the

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Mishra, Amrita. "A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 2, no. 2 (2021): 1–10. http://dx.doi.org/10.54060/jieee/002.02.009.

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Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positi

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Shah, Sharvil, Kannan Kumar, and Ra K. Sarvananguru. "Sentimental Analysis of Twitter Data using Classifier Algorithms." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 357. http://dx.doi.org/10.11591/ijece.v6i1.8982.

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Microblogging has become a daily routine for most of the people in this world. With the help of Microblogging people get opinions about several things going on, not only around the nation but also worldwide. Twitter is one such online social networking website where people can post their views regarding something. It is a huge platform having over 316 Million users registered from all over the world. It enables users to send and read short messages with over 140 characters for compatibility with SMS messaging. A good sentimental analysis of data of this huge platform can lead to achieve many n

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Shah, Sharvil, Kannan Kumar, and Ra K. Sarvananguru. "Sentimental Analysis of Twitter Data using Classifier Algorithms." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 357. http://dx.doi.org/10.11591/ijece.v6i1.pp357-366.

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Microblogging has become a daily routine for most of the people in this world. With the help of Microblogging people get opinions about several things going on, not only around the nation but also worldwide. Twitter is one such online social networking website where people can post their views regarding something. It is a huge platform having over 316 Million users registered from all over the world. It enables users to send and read short messages with over 140 characters for compatibility with SMS messaging. A good sentimental analysis of data of this huge platform can lead to achieve many n

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You, Zi-Hung, Ya-Han Hu, Chih-Fong Tsai, and Yen-Ming Kuo. "Integrating Feature and Instance Selection Techniques in Opinion Mining." International Journal of Data Warehousing and Mining 16, no. 3 (2020): 168–82. http://dx.doi.org/10.4018/ijdwm.2020070109.

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Opinion mining focuses on extracting polarity information from texts. For textual term representation, different feature selection methods, e.g. term frequency (TF) or term frequency–inverse document frequency (TF–IDF), can yield diverse numbers of text features. In text classification, however, a selected training set may contain noisy documents (or outliers), which can degrade the classification performance. To solve this problem, instance selection can be adopted to filter out unrepresentative training documents. Therefore, this article investigates the opinion mining performance associated

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Mujahid, Muhammad, Ernesto Lee, Furqan Rustam, et al. "Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19." Applied Sciences 11, no. 18 (2021): 8438. http://dx.doi.org/10.3390/app11188438.

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Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects

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Jadhav, Varsha D., and Sachin N. Deshmukh. "Twitter Intention Classification Using Bayes Approach for Cricket Test Match Played Between India and South Africa 2015." International Journal of Rough Sets and Data Analysis 4, no. 2 (2017): 49–62. http://dx.doi.org/10.4018/ijrsda.2017040104.

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Information retrieval and forecasting in real time is becoming the fastest and most efficient way to obtain useful knowledge of what is happening now, allowing organizations to react quickly when problem appears which help to improve their performance. There is enormous amount of data in the form of tweets. It builds data processing system that creates informative data about the cricket test matches. Using twitter data, the authors find the sentiments or polarity of fans posting tweets related to game. Polarity is given as positive, negative and neutral. The authors also analyze the feelings o

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Ali, Manal Mostafa. "Arabic sentiment analysis about online learning to mitigate covid-19." Journal of Intelligent Systems 30, no. 1 (2021): 524–40. http://dx.doi.org/10.1515/jisys-2020-0115.

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Abstract The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National Research Council Canada

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Mahmood, Sozan Abdulla, and Qani Qabil Qasim. "Big Data Sentimental Analysis Using Document to Vector and Optimized Support Vector Machine." UHD Journal of Science and Technology 4, no. 1 (2020): 18. http://dx.doi.org/10.21928/uhdjst.v4n1y2020.pp18-28.

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With the rapid evolution of the internet, using social media networks such as Twitter, Facebook, and Tumblr, is becoming so common that they have made a great impact on every aspect of human life. Twitter is one of the most popular micro-blogging social media that allow people to share their emotions in short text about variety of topics such as company’s products, people, politics, and services. Analyzing sentiment could be possible as emotions and reviews on different topics are shared every second, which makes social media to become a useful source of information in different fields such as

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Pimpalkar, Amit Purushottam, and R. Jeberson Retna Raj. "Influence of Pre-Processing Strategies on the Performance of ML Classifiers Exploiting TF-IDF and BOW Features." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 9, no. 2 (2020): 49–68. http://dx.doi.org/10.14201/adcaij2020924968.

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Data analytics and its associated applications have recently become impor-tant fields of study. The subject of concern for researchers now-a-days is a massive amount of data produced every minute and second as people con-stantly sharing thoughts, opinions about things that are associated with them. Social media info, however, is still unstructured, disseminated and hard to handle and need to be developed a strong foundation so that they can be utilized as valuable information on a particular topic. Processing such unstructured data in this area in terms of noise, co-relevance, emoticons, folks

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Shah, Harshil. "Twitter Sentiment Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 12 (2018): 15. http://dx.doi.org/10.23956/ijarcsse.v7i12.493.

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With the increasing popularity of social media, people have begun to express their opinions on a variety of topics on Twitter and other similar services.Sentiment Analysis on tweets has gained much attention for gathering public opinions on a wide variety of topics. In this paper, we aim to tackle the one of the fundamental problems of sentiment analysis, sentiment polarity categorization. We present a hybrid approach for identifying sentiments from a given piece of text.

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Gupta, Itisha, and Nisheeth Joshi. "A Review on Negation Role in Twitter Sentiment Analysis." International Journal of Healthcare Information Systems and Informatics 16, no. 4 (2021): 1–19. http://dx.doi.org/10.4018/ijhisi.20211001.oa14.

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Negation is an important linguistic phenomenon that needs to be considered for identifying correct sentiments from the opinionated data available in digital form. It has the power to alter the polarity or strength of the polarity of affected words. In this paper, the authors present a survey on the negation role that has been done until now in sentiment analysis, specifically Twitter sentiment analysis. The authors discuss the various approaches of modelling negation in Twitter sentiment analysis. In particular, their focus is on negation scope detection and negation handling methods. This art

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Dong, Li, Furu Wei, Shujie Liu, Ming Zhou, and Ke Xu. "A Statistical Parsing Framework for Sentiment Classification." Computational Linguistics 41, no. 2 (2015): 293–336. http://dx.doi.org/10.1162/coli_a_00221.

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We present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that use syntactic parsing results for sentiment analysis, we develop a statistical parser to directly analyze the sentiment structure of a sentence. We show that complicated phenomena in sentiment analysis (e.g., negation, intensification, and contrast) can be handled the same way as simple and straightforward sentiment expressions in a unified and probabilistic way. We formulate the sentiment grammar upon Context-Free Grammars (CFGs), and provide a formal description

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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 tw

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Kocak, Bahri BAran, and Inci Polat. "Determination of twitter users sentiment polarity toward airline market." Pressacademia 2, no. 1 (2016): 684. http://dx.doi.org/10.17261/pressacademia.2016118690.

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Shafina Banu, S., K. Syed Kousar Niasi, and E. Kannan. "Classification Techniques on Twitter Data: A Review." Asian Journal of Computer Science and Technology 8, S2 (2019): 66–69. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2022.

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Data mining is the practice of examining unknown patterns of data according to diverse viewpoints for classification into valuable information, which is composed and gathered in collective areas, such as data warehouses.For effective analysis, data mining algorithms enabling business decision making and other information necessities to eventually cut costs and raise revenue. Sentiment analysis is the method of defining the emotional tone behind a sequence of words, used to gain an accepting of the attitudes, opinions and emotions conveyed within an online mention. Sentiment analysis is tremend

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Kanavos, Andreas, Nikolaos Nodarakis, Spyros Sioutas, Athanasios Tsakalidis, Dimitrios Tsolis, and Giannis Tzimas. "Large Scale Implementations for Twitter Sentiment Classification." Algorithms 10, no. 1 (2017): 33. http://dx.doi.org/10.3390/a10010033.

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Li, Mengdi, Eugene Ch’ng, Alain Yee Loong Chong, and Simon See. "Multi-class Twitter sentiment classification with emojis." Industrial Management & Data Systems 118, no. 9 (2018): 1804–20. http://dx.doi.org/10.1108/imds-12-2017-0582.

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Purpose Recently, various Twitter Sentiment Analysis (TSA) techniques have been developed, but little has paid attention to the microblogging feature – emojis, and few works have been conducted on the multi-class sentiment analysis of tweets. The purpose of this paper is to consider the popularity of emojis on Twitter and investigate the feasibility of an emoji training heuristic for multi-class sentiment classification of tweets. Tweets from the “2016 Orlando nightclub shooting” were used as a source of study. Besides, this study also aims to demonstrate how mapping can contribute to interpre

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Srivastava, Ankit, Vijendra Singh, and Gurdeep Singh Drall. "Sentiment Analysis of Twitter Data." International Journal of Healthcare Information Systems and Informatics 14, no. 2 (2019): 1–16. http://dx.doi.org/10.4018/ijhisi.2019040101.

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Over the past few years, the novel appeal and increasing popularity of social networks as a medium for users to express their opinions and views have created an accumulation of a massive amount of data. This evolving mountain of data is commonly termed Big Data. Accordingly, one area in which the application of new techniques in data mining research has significant potential to achieve more precise classification of hidden knowledge in Big Data is sentiment analysis (aka optimal mining). A hybrid approach using Naïve Bayes and Random Forest on mining Twitter datasets is presented here as an ex

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Murthy, Jamuna S., G. M. Siddesh, and K. G. Srinivasa. "TwitSenti: A Real-Time Twitter Sentiment Analysis and Visualization Framework." Journal of Information & Knowledge Management 18, no. 02 (2019): 1950013. http://dx.doi.org/10.1142/s0219649219500138.

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Twitter is considered as one of the world’s largest social networking sites which allow users to customize their public profile, connect with others and interact with connected users. The proposed work introduces a distributed real-time twitter sentiment analysis and visualization framework by implementing novel algorithms for twitter sentiment analysis called Emotion-Polarity-SentiWordNet. The framework is applied to build an interactive web application called “TwitSenti” which can benefit companies and other organizations in knowing the people’s sentiment towards the aspects such as brands,

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Et. al., Rushali Deshmukh,. "Stock Prediction by using NLP and Deep Learning Approach." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 202–11. http://dx.doi.org/10.17762/turcomat.v12i1s.1611.

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People have a tendency to analyze existing strategies and so planned new strategies for inventory prediction. We have used Sentiment evaluation and Technical evaluation through NLP and Deep mastering approach. In order to exploit benefits of sentiment analysis on enterprise associated inventory, we have proposed a model that will use the sentiment analysis on twits associated with special sectors that are Information Technology sector, Banking sector, Pharmaceutical sector, Automobile sector, Infrastructure sector which are extracted from twitter. These twits are extracted from twitter for cal

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Misopoulos, Fotis, Miljana Mitic, Alexandros Kapoulas, and Christos Karapiperis. "Uncovering customer service experiences with Twitter: the case of airline industry." Management Decision 52, no. 4 (2014): 705–23. http://dx.doi.org/10.1108/md-03-2012-0235.

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Purpose – In this paper the authors present a study that uses Twitter to identify critical elements of customer service in the airline industry. The goal of the study was to uncover customer opinions about services by monitoring and analyzing public Twitter commentaries. The purpose of this paper is to identify elements of customer service that provide positive experiences to customers as well as to identify service processed and features that require further improvements. Design/methodology/approach – The authors employed the approach of sentiment analysis as part of the netnography study. Th

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