To see the other types of publications on this topic, follow the link: Sentiment Analysis.
Journal articles on the topic 'Sentiment Analysis'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Sentiment Analysis.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Haidar, Abdullah, and Putri Oktavia Rusadi. "A Sentiment Analysis: History of Islamic Economic Thought." Journal of Islamic Economics (JoIE) 2, no. 2 (October 31, 2022): 150–63. http://dx.doi.org/10.21154/joie.v2i2.5082.
Full textAbstract:
This study reviews the history of Islamic economic thought research in Islamic economics and finance. It uses descriptive statistical analysis based on selected 125 article publications. The entire sample publications have been published from 1984 to 2022. This study analyzes the number of publications based on journal and year, the top authors, the top-cited paper, and the sentiment analysis. The results show that the research of the history of Islamic economic thought throughout the world has a high-positive sentiment of 1%, a positive sentiment of 27%, a negative sentiment of 33%, a high-negative sentiment of 1%, and the rest have a neutral sentiment of 38%. Also, the number of sentiments for these studies has increased in the world community; the most significant number of high-positive sentiments occurred in 2021, with one publication sentiment. Then the most significant number of positive sentiments occurred in 2019, with as many as seven published article sentiments. The most significant number of neutral sentiments occurred in 2018, the same as positive sentiments, seven published article sentiments, and the most significant number of negative sentiments occurred in 2020, six published article sentiments.Penelitian ini mencoba mengkaji sejarah penelitian pemikiran ekonomi Islam di bidang ekonomi dan keuangan Islam. Ini menggunakan analisis statistik deskriptif berdasarkan 125 publikasi artikel yang dipilih. Seluruh sampel publikasi telah diterbitkan dari tahun 1984 hingga 2022. Studi ini menganalisis jumlah publikasi berdasarkan jurnal dan tahun, penulis teratas, makalah yang dikutip teratas, dan analisis sentimen. Hasil penelitian menunjukkan bahwa penelitian sejarah pemikiran ekonomi Islam di seluruh dunia memiliki sentimen positif tinggi 1%, sentimen positif 27%, sentimen negatif 33%, sentimen negatif tinggi 1%, dan selebihnya. memiliki sentimen netral sebesar 38%. Selain itu, jumlah sentimen untuk studi ini telah meningkat di masyarakat dunia, jumlah sentimen positif tinggi terbesar terjadi pada tahun 2021 dengan satu sentimen artikel publikasi. Kemudian jumlah sentimen positif terbesar terjadi pada tahun 2019, yaitu sebanyak tujuh artikel sentimen yang dipublikasikan. Jumlah sentimen netral terbesar terjadi pada tahun 2018, sama dengan sentimen positif yaitu sebanyak tujuh sentimen artikel yang dipublikasikan, dan jumlah sentimen negatif terbesar terjadi pada tahun 2020 yaitu sebanyak enam sentimen artikel yang dipublikasikan.
2
Datt, Jivat Singh. "SENTIMENT ANALYSIS USING CUSTOMER FEEDBACK." International Journal of Trendy Research in Engineering and Technology 07, no. 04 (2023): 09–13. http://dx.doi.org/10.54473/ijtret.2023.7402.
Full textAbstract:
This Sentiment analysis is one of the fastest spreading research areas in computer science, making it challenging to keep track of all the activities in the area. We present customer feedback reviews on products, where we utilize opinion mining, text mining and sentiments, which has affected the surrounded world by changing their opinion on a specific product. Data used in this study are online product reviews collected from Amazon.com. We performed a comparative sentiment analysis of retrieved reviews. This research paper provides you with sentimental analysis of various smart phone opinions on smart phones dividing them Positive, Negative and Neutral Behavior.
3
Li, Jiangfeng, Ziyu Li, Xiaofeng Ma, Qinpei Zhao, Chenxi Zhang, and Gang Yu. "Sentiment Analysis on Online Videos by Time-Sync Comments." Entropy 25, no. 7 (July 2, 2023): 1016. http://dx.doi.org/10.3390/e25071016.
Full textAbstract:
Video highlights are welcomed by audiences, and are composed of interesting or meaningful shots, such as funny shots. However, video shots of highlights are currently edited manually by video editors, which is inconvenient and consumes an enormous amount of time. A way to help video editors locate video highlights more efficiently is essential. Since interesting or meaningful highlights in videos usually imply strong sentiments, a sentiment analysis model is proposed to automatically recognize sentiments of video highlights by time-sync comments. As the comments are synchronized with video playback time, the model detects sentiment information in time series of user comments. Moreover, in the model, a sentimental intensity calculation method is designed to compute sentiments of shots quantitatively. The experiments show that our approach improves the F1 score by 12.8% and overlapped number by 8.0% compared with the best existing method in extracting sentiments of highlights and obtaining sentimental intensities, which provides assistance for video editors in editing video highlights efficiently.
4
Li, Fangtao, Minlie Huang, and Xiaoyan Zhu. "Sentiment Analysis with Global Topics and Local Dependency." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1371–76. http://dx.doi.org/10.1609/aaai.v24i1.7523.
Full textAbstract:
With the development of Web 2.0, sentiment analysis has now become a popular research problem to tackle. Recently, topic models have been introduced for the simultaneous analysis for topics and the sentiment in a document. These studies, which jointly model topic and sentiment, take the advantage of the relationship between topics and sentiment, and are shown to be superior to traditional sentiment analysis tools. However, most of them make the assumption that, given the parameters, the sentiments of the words in the document are all independent. In our observation, in contrast, sentiments are expressed in a coherent way. The local conjunctive words, such as “and” or “but”, are often indicative of sentiment transitions. In this paper, we propose a major departure from the previous approaches by making two linked contributions. First, we assume that the sentiments are related to the topic in the document, and put forward a joint sentiment and topic model, i.e. Sentiment-LDA. Second, we observe that sentiments are dependent on local context. Thus, we further extend the Sentiment-LDA model to Dependency-Sentiment-LDA model by relaxing the sentiment independent assumption in Sentiment-LDA. The sentiments of words are viewed as a Markov chain in Dependency-Sentiment-LDA. Through experiments, we show that exploiting the sentiment dependency is clearly advantageous, and that the Dependency-Sentiment-LDA is an effective approach for sentiment analysis.
5
Ginabila, Ginabila, and Ahmad Fauzi. "Analisis Sentimen Terhadap Pemutar Musik Online Spotify Dengan Algoritma Naive Bayes dan Support Vector Machine." Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika 6, no. 2 (July 20, 2023): 111–22. http://dx.doi.org/10.47324/ilkominfo.v6i2.180.
Full textAbstract:
Abstrak: Manusia memiliki kebutuhan preferensi musik yang yang sangat beragam, oleh karena itu pemutar musik online menjadi salah satu solusi untuk memenuhi kebutuhan ini dengan menyediakan katalog musik yang luas. Analisis sentimen adalah proses untuk mengevaluasi dan mengklasifikasikan sentimen atau perasaan di balik teks atau data yang diberikan. Dalam konteks ini, analisis sentimen dilakukan pada pemutar musik online Spotify. Dua algoritma yang umum digunakan untuk analisis sentimen adalah Naive Bayes dan Support Vector Machine (SVM). Kedua algoritma ini dapat diterapkan dalam analisis sentimen pada pemutar musik online. Data teks seperti ulasan atau komentar pengguna dikumpulkan dan dilabeli dengan sentimen yang sesuai. Hasil dari penelitian menggunakan kedua algoritma ini menghasilkan nilai akurasi yang hampir sama baiknya. Algoritma Support Vector Machine menghasilkan tingkat akurasi sebesar 82,42%, sedangkan untuk Algoritma Naive Bayes mencapai 84,73%.Kata kunci: Analisis Sentimen, Naive Bayes, Support Vector MachineAbstract: Humans have diverse music preferences and online music players are a solution to meet these needs by providing a wide music catalog. Sentiment analysis is the process of evaluating and classifying sentiments or feelings behind given texts or data. In this context, sentiment analysis is performed on Spotify online music players. Two common algorithms used for sentiment analysis are Naive Bayes and Support Vector Machine (SVM). Both algorithms can be applied in sentiment analysis for online music players. Text data such as user reviews or comments are collected and labeled with corresponding sentiments. The results of the research using both algorithms yielded similar high accuracy. The Support Vector Machine algorithm achieved an accuracy rate of 82.42%, while the Naive Bayes algorithm reached 84.73%.Keywords: Sentiment Analysis, Naive Bayes, Support Vector Machine
6
Tittu T, Anoush, Rakshitha K, Nanditha TN, Sireya Rani M, and Yukthi S R. "STOCK MARKET ANALYSIS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (January 15, 2024): 1–10. http://dx.doi.org/10.55041/ijsrem28108.
Full textAbstract:
The abstract aims to address the correlation between stock market movements and public sentiments expressed on Twitter. It delves into the utilization of sentiment analysis and supervised machine learning techniques to explore this connection. The study leverages Word2vec for textual representation, examining how shifts in stock prices align with sentiments expressed in tweets about specific companies. The investigation underscores the potential impact of positive news and social media sentiments on stock prices, emphasizing a demonstrated correlation between fluctuations in stock prices and sentiments conveyed in Twitter.- Keywords: Hashtag Collection, Data Collection, Real-Time Stock History Data, Positive Keywords, Negative Keywords, Polarity Computation, Sentiment Analysis , Sentiment Index Computation, Sentiment Discrepancy Index, Price Prediction, Yahoo Finance API
7
K. Divya and Mrs P. Menaka. "Twitter Sentiment Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (March 15, 2025): 1305–10. https://doi.org/10.32628/cseit25112465.
Full textAbstract:
Sentiment analysis deals with relating and classifying opinions or sentiments expressed in source textbook. A significant quantum of sentiment-rich data is being produced via social media in the form of tweets, status updates, blog entries, and other content. Understanding the opinions of the millions can be greatly served from sentiment analysis of this stoner- generated data. Twitter sentiment analysis is more grueling than general sentiment analysis because of the frequence of misspellings and shoptalk expressions. Twitter allows a character count of over to 140 characters. The two approaches employed to assay sentiments from the textbook are the knowledge base fashion and the machine literacy approach. Analysing sentiments help in understanding how people are allowing emotionally and classifying it as negative, positive or neutral. The dataset used is a collection of tweets related to the brand apple. Two different machine learning classifiers are used then, so that a person's sentiment can be linked. These classifiers are applied and also the stylish classifier with the stylish result will be chosen in order to prognosticate people's feelings. Professionals will be better suitable to assess people's feelings and fete early signs of torture through this analysis.
8
Karuna, G., Pavuluri Anvesh, Chiranji Sharath Singh, Kommula Ruthvik Reddy, Praveen Kumar Shah, and S. Siva Shankar. "Feasible Sentiment Analysis of Real Time Twitter Data." E3S Web of Conferences 430 (2023): 01045. http://dx.doi.org/10.1051/e3sconf/202343001045.
Full textAbstract:
Sentiment analysis plays a significant role in understanding public opinion, trends, and sentiments expressed on social media platforms. In this paper, we focus on performing sentiment analysis on real-time Twitter data to gain insights into the sentiments related to specific topics or events, we collect a stream of tweets based on predefined keywords or hashtags. The collected tweets undergo pre-processing steps to clean and standardize the text for sentiment analysis. We employ machine learning classify the sentiments expressed in tweets, utilizing sentiment lexicons and training data as references. Real-time sentiment analysis is performed as new tweets are collected, enabling continuous monitoring and analysis of public sentiment. The sentiment analysis results are visualized through informative visualizations such as sentiment distribution charts and sentiment trends over time. Additionally, we focus on topic-specific analysis by filtering tweets based on relevant keywords or hashtags, providing deeper insights into sentiments related to specific subjects. The paper faces challenges such as noisy and informal text, ambiguity in sentiment expression, and handling large volumes of real-time data. Addressing these challenges, we aim to develop an effective sentiment analysis system that provides valuable insights into public sentiment and supports decision-making processes in various domains.
9
Qu, Saiying. "A Thematic Analysis of English and American Literature Works Based on Text Mining and Sentiment Analysis." Journal of Electrical Systems 20, no. 6s (April 29, 2024): 1575–86. http://dx.doi.org/10.52783/jes.3076.
Full textAbstract:
A theme analysis model integrating text mining and sentiment analysis has emerged as a powerful tool for understanding English and American literary works. By employing techniques such as topic modeling, keyword extraction, and sentiment analysis, this model can identify recurring themes, motifs, and emotional tones within texts. Through text mining, it extracts key concepts and topics, while sentiment analysis discerns the underlying emotions conveyed by the authors. By combining these approaches, researchers can uncover deeper insights into the thematic elements and cultural contexts of English and American literature. This paper explores the application of text mining and sentiment analysis techniques to analyze a dataset comprising American literary works. With computational methods such as bi-gram analysis, multimodal feature extraction, and sentiment analysis using the Bi-gram Multimodal Sentimental Analysis (Bi-gramMSA) approach. With the proposed Bi-gramMSA model the multimodal features in the American Literature are examined to investigate the thematic, emotional, and multimodal aspects of the literature. Through our analysis, we uncover significant bi-grams, extract multimodal features, and assess sentiment distribution across the texts. The results highlight the effectiveness of these computational methodologies in uncovering patterns, sentiments, and features within the literary corpus. The proposed Bi-gramMSA model achives a higher score for the different scores in the Chinese Literature.
10
Sunil Kumar, V., Vedashree C.R, and Sowmyashree S. "IMAGE SENTIMENTAL ANALYSIS: AN OVERVIEW." International Journal of Advanced Research 10, no. 03 (March 31, 2022): 361–70. http://dx.doi.org/10.21474/ijar01/14398.
Full textAbstract:
Visual content, such as photographs and video, contains not only objects, locations, and events, but also emotional and sentimental clues. On social networking sites, images are the simplest way for people to communicate their emotions. Images and videos are increasingly being used by social media users to express their ideas and share their experiences. Sentiment analysis of such large-scale visual content can aid in better extracting user sentiments toward events or themes, such as those in image tweets, so that sentiment prediction from visual content can be used in conjunction with sentiment analysis of written content. Despite the fact that this topic is relatively new, a wide range of strategies for various data sources and challenges have been created, resulting in a substantial body of study. This paper introduces the area of Image Sentiment Analysis and examines the issues that it raises. A description of new obstacles is also included, as well as an assessment of progress toward more sophisticated systems and related practical applications, as well as a summary of the studys findings.
11
Mehra, Prof Richa, Diksha Saxena, and Shubham Gupta Joy Joseph. "Sentiment Analysis." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1370–73. http://dx.doi.org/10.31142/ijtsrd23375.
Full text
12
Bhatt, Ruchi, and Prinima Gupta. "Sentiment Analysis." Indian Journal of Science and Technology 12, no. 41 (November 20, 2019): 1–6. http://dx.doi.org/10.17485/ijst/2019/v12i41/145556.
Full text
13
Nagane, Mr Sanket K., Mr Prashant S. Pawar, and Prof V. V. Godase. "Cinematica Sentiment Analysis." Journal of Image Processing and Intelligent Remote Sensing, no. 23 (May 30, 2022): 27–32. http://dx.doi.org/10.55529/jipirs.23.27.32.
Full textAbstract:
Sentiment analysis, a subfield of natural language processing, holds significant importance in understanding the emotions and opinions expressed in textual data. This project focuses on applying sentiment analysis techniques to movie reviews, aiming to develop an efficient model for automatically classifying sentiments as positive, negative, or neutral. The primary goal of this project is to create a robust sentiment analysis of accurately categorizing the sentiment conveyed in movie reviews. By leveraging machine learning algorithms and natural language processing techniques, the model aims to provide insights into audience reactions and contribute to the broader field of sentiment analysis.
14
Gunawan, Yogi, Iwan Purnama, and Rohani Rohani. "Sentiment Analysis of Twitter towards the 2024 Indonesian Presidential Candidates Using the Naïve Bayes Algorithms." International Journal of Science, Technology & Management 5, no. 4 (July 30, 2024): 953–61. http://dx.doi.org/10.46729/ijstm.v5i4.1154.
Full textAbstract:
The increasing use of social media (Twitter) has made it a platform for the public to express their views on the Indonesian presidential candidate in the 2024 elections. The sentiment expressed through comments on Twitter provides important insights into the public perception of the candidates. However, given the volume and speed at which information is disseminated on social media, manual analysis of this sentiment becomes impractical. Therefore, the use of the Naïve Bayes algorithm for automatic sentiment analysis is considered essential to understanding voter support and preferences. The study aims to analyze Twitter users' sentiments towards three Indonesian presidential candidates in 2024, Anies, Ganjar, and Prabowo, using the Naïve Bayes algorithm. We categorize the results of this analysis into three sentiment categories: positive, negative, and neutral. The methods used in the study involved collecting Twitter comment data related to the three candidates, pre-processing data, labeling data, applying the Naïve Bayes algorithm for the classification of sentiment, and evaluation of the performance of the algorithm performed by calculating the level of accuracy. The results of the research showed that the Naïve Bayes algorithm was able to classify sentiments with fairly high precision, namely 75.54% for Anies, 82.74% for Ganjar, and 75.24% for Prabowo. The conclusion of this study is that sentimental analysis using the Naïve Bayes algorithm can provide significant insights into voter preferences and support. The sentimental data generated can serve as a strong foundation for decision-makers to design campaign strategies that are more effective and responsive to public perception. This research also opens up opportunities for further development in the use of sentimental analysis techniques in politics and campaigns.
15
Wu, Yuanyuan. "Evaluation of Innovation and Entrepreneurship Culture Atmosphere in Universities Based on Sentiment Analysis." Journal of Electrical Systems 20, no. 3s (March 31, 2024): 1863–73. http://dx.doi.org/10.52783/jes.1725.
Full textAbstract:
The entrepreneurship culture atmosphere in universities plays a pivotal role in shaping the mindset and behavior of students towards innovation and risk-taking. Through sentiment analysis, universities can gauge the prevailing attitudes and emotions towards entrepreneurship among students and faculty members. A positive entrepreneurship culture atmosphere fosters an environment of creativity, resilience, and collaboration, encouraging students to pursue entrepreneurial ventures and take calculated risks. By promoting a supportive ecosystem that celebrates failure as a learning opportunity and provides resources and mentorship for budding entrepreneurs, universities can cultivate an entrepreneurial spirit among their community members. Moreover, sentiment analysis enables universities to identify areas for improvement and tailor interventions to enhance the entrepreneurship culture atmosphere further. This paper presents an evaluation of the innovation and entrepreneurship culture atmosphere in universities using sentiment analysis, enhanced by Automated Recommender Probabilistic bi-gram Sentimental Analysis (ARPbi-gSA). The research aims to assess the prevailing sentiments and attitudes towards innovation and entrepreneurship among students and faculty members within university communities. Through the analysis of textual data from various sources such as social media posts, surveys, and academic literature, the ARPbi-gSA algorithm evaluates the sentiment expressed towards entrepreneurship initiatives, programs, and events. Results from sentiment analysis provide insights into the overall positivity or negativity surrounding the entrepreneurship culture atmosphere, identifying strengths and areas for improvement. The sentiment analysis conducted using ARPbi-gSA revealed a sentiment score of 0.75, indicating a predominantly positive sentiment towards entrepreneurship initiatives within the university. Out of 1000 social media posts analyzed, 650 expressed positive sentiments, while 250 were neutral, and 100 were negative, reflecting an overall positive sentiment towards entrepreneurship culture. Based on the sentiment analysis findings, the ARPbi-gSA algorithm provided recommendations for enhancing the entrepreneurship culture atmosphere, resulting in a 15% increase in student participation in entrepreneurial activities over the academic year.
16
Wang, Xinzhi, Hui Zhang, and Zheng Xu. "Public Sentiments Analysis Based on Fuzzy Logic for Text." International Journal of Software Engineering and Knowledge Engineering 26, no. 09n10 (November 2016): 1341–60. http://dx.doi.org/10.1142/s0218194016400076.
Full textAbstract:
Sentiment analysis from microblog platform has received an increasing interest from web mining community in recent years. Current sentiment analysis methods are mainly based on the hypothesis that each word expresses only one sentiment. However, human sentiment are prototyped and fuzzy-confined as declared in social psychology, which is conflicting with the hypothesis. This is one of the barriers that impede the computation of complex public sentiment of web events in microblog. Therefore, how to find a reasonable computational model, combining learning technology and human sentiment cognition theory, is a novel idea in event sentiment analysis of microblog. In this paper, a new sentiment computation approach, which is defined as public sentiments discriminator (PSD), considering both fuzzy logic and sentiment complexity, is proposed. Unlike traditional machine learning methods, PSD is based on the rational hypothesis that sentiments are correlated with each other. A three-level computing structure, sentiment-term level, microblog level and public sentiment level, is employed. Experiments show that the proposed approach, PSD, can achieve similar accuracy and [Formula: see text]1-measure but more cognitive results when compared with traditional well-known machine learning methods. These experimental studies have confirmed that PSD can generate an interpretable result with no restriction among sentiments.
17
Narasimham, Ayalasomayajula Appala. "SENTIMENTAL ANALYSIS ON TOURISM REVIEWS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 29, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32368.
Full textAbstract:
Sentiment analysis plays a pivotal role in understanding the sentiments and opinions expressed in textual data, offering valuable insights into various domains, including tourism. In this study, we present a comprehensive review of sentiment analysis techniques applied to tourism reviews using machine learning algorithms. The abundance of user-generated content on tourism platforms has made sentiment analysis an indispensable tool for businesses and researchers alike. By leveraging machine learning algorithms, researchers can extract sentiments from vast amounts of textual data efficiently and accurately. This review outlines the key methodologies and approaches utilized in sentiment analysis of tourism reviews. It discusses preprocessing techniques such as text tokenization, stop-word removal, and stemming, which are crucial for preparing textual data for analysis. Furthermore, it examines various machine learning algorithms employed for sentiment classification, including Naive Bayes, Support Vector Machines, and Recurrent Neural Networks. Additionally, the review delves into feature extraction methods such as bag-of-words, TF-IDF, and word embeddings, highlighting their impact on sentiment analysis accuracy. Moreover, it explores the challenges and limitations associated with sentiment analysis in the tourism domain, such as sarcasm detection and language nuances.
18
Shah, Harshil. "Twitter Sentiment Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 12 (January 3, 2018): 15. http://dx.doi.org/10.23956/ijarcsse.v7i12.493.
Full textAbstract:
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.
19
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 (August 1, 2020): 47–62. http://dx.doi.org/10.14738/tmlai.84.8274.
Full textAbstract:
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.
20
Bere, Mery Ernawati, Yoseph Pius Kurniawan Kelen, Hevi Herlina Ullu, and Budiman Baso. "Implementasi Algoritma Naive Bayes Classifier Terhadap Analisis Sentimen Kondisi Stunting di Indonesia Pada Media Sosial X." Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) 7, no. 4 (July 23, 2024): 598–605. https://doi.org/10.32672/jnkti.v7i4.7752.
Full textAbstract:
Abstrak—Media sosial X merupakan plat form media sosial umum digunakan user untuk berkomunikasi dan menyebarkan informasi berupa tweets. Penelitian ini bertujuan untuk menganalisis sentimen terhadap kondisi Stunting di Indonesia pada media sosial X menggunakan metode Naïve Bayes Classifier dan mengklasifikasinya menjadi tiga kelas yaitu Negatif, Positif, dan Netral. Dengan dibuatnya analisis sentimen kondisi Stunting diIndonesia adalah untuk mempermudah dalam menganalisis ketiga sentimen yaitu Positif Negatif dan Netral. Berdasarkan hasil penelitian untuk mengetahui polaritas sentimen mengenai kondisi stunting diIndonesia pada media sosial X dengan menggunakan metode Naïve Bayes Classifier untuk menhasilkan polaritas dari data training serta menguji akurasi model probabilitas dengan data testing. Berdasarkan hasil analisis sentimen terhadap kondisi stunting diIndonesia didapatkan sentimen positif lebih dominan yaitu sebanyak 526 data, diikuti oleh sentimen negatif 340 data dan sentimen netral 134 data. Setelah proses klasifikasi naïve bayes dilakukan hasil data uji didapatkan sentimen positif sebesar 0.79, sentimen negatif 0,64. Hal ini menandakan bahwa hasil pengujian terhadap data uji dari sentimen komentar pengguna media sosial X Masyarakat Indonesia memiliki representasi nilai positif yang lebih tinggi terkait kasus stunting yang ada diindonesia.Kata kunci: Stunting, Naïve Bayes Classifier Abstract —Social media X is form plateSocial media is commonly used by users to communicate and spread information in the form of tweets. This research aims to analyze sentiment towards the condition of Stunting in Indonesia on social media X using the Naïve Bayes Classifier method and classify it into three classes, namely Negative, Positive and Neutral. By creating a sentiment analysis of Stunting conditions in Indonesia, it is to make it easier to analyze the three sentiments, namely Positive Negative and Neutral. Based on the results of research to determine the polarity of sentiment regarding stunting conditions in Indonesia on social media X using the Naïve Bayes Classifier method to produce polarity from training data and test the accuracy of the probability model with testing data. Based on the results of sentiment analysis regarding stunting conditions in Indonesia, it was found that positive sentiment was more dominant, namely 526 data, followed by negative sentiment 340 data and neutral sentiment 134 data. After the naïve Bayes classification process was carried out, the test data results obtained positive sentiment of 0.79, negative sentiment of 0.64. This indicates that the test results on test data from the sentiment of comments from social media user X Indonesian society have a higher representation of positive values regarding stunting cases in Indonesia.Keywords: Stunting, Naïve Bayes Classifier
21
Busst, Mikail Bin Muhammad Azman, Kalaiarasi Sonai Muthu Anbananthen, and Subarmaniam Kannan. "Aspect-Level Sentiment Analysis through Aspect-Oriented Features." HighTech and Innovation Journal 5, no. 1 (March 1, 2024): 109–28. http://dx.doi.org/10.28991/hij-2024-05-01-09.
Full textAbstract:
Aspect-level sentiment analysis is essential for businesses to comprehend sentiment polarities associated with various aspects within unstructured texts. Although several solutions have been proposed in recent studies in sentiment analysis, a few challenges persist. A significant challenge is the presence of multiple aspects within a single written text, each conveying its own sentiments. Besides this, the exploration of ensemble learning in the existing literature is limited. Therefore, this study proposes a novel aspect-level sentiment analysis solution that utilizes an ensemble of Bidirectional Long Short-Term Memory (BiLSTM) models. This innovative solution extracts aspects and sentiments and incorporates a rule-based algorithm to combine accurate sets of aspect and sentiment features. Experimental analysis demonstrates the effectiveness of the proposed methodology in accurately extracting aspect-level sentiment features from input texts. The proposed solution was able to obtain an F1 score of 92.98% on the SemEval-2014 Restaurant dataset when provided with the correct set of aspect-level sentiment features and an F1 score of 95.54% on the SemEval-2016 Laptop dataset when provided with the aspect-level sentiment features generated by the aspect-sentiment mapper algorithm. Doi: 10.28991/HIJ-2024-05-01-09 Full Text: PDF
22
Kumar, Ravindra. "Methods to Perform Opinion Mining and Sentiment Analysis to Detect Factors Affecting Mental Health." International Journal of Engineering and Advanced Technology 11, no. 1 (October 30, 2021): 70–72. http://dx.doi.org/10.35940/ijeat.f3025.1011121.
Full textAbstract:
Sentimental analysis and opinion extraction are emerging fields at AI. These approaches help organizations to use the opinions, sentiments, and subjectivity of their consumers in decision-making. Sentiments, views, and opinions show the feeling of the consumers towards a given product or service. In recent years, Opinion Mining and Sentiment Analysis has become an important tool to detect the factors affecting mental health. It’s Also true that human biasness is available in giving opinions, but it can be eliminated through the use of algorithms to get better results. However, it is crucial to remember that the developers are human and might pass the biasness to the algorithms during training. The main target of this paper is to give background knowledge on opinion extraction and sentimental analysis and how factors affecting mental health can be collected. The paper aimed to use interested individuals in knowing some of the algorithms in opinions extraction and sentimental analysis. The paper also provides benefits of using sentiment analysis and some of the challenges of using the algorithms.
23
Wu, Lifang, Sinuo Deng, Heng Zhang, and Ge Shi. "Sentiment Interaction Distillation Network for Image Sentiment Analysis." Applied Sciences 12, no. 7 (March 29, 2022): 3474. http://dx.doi.org/10.3390/app12073474.
Full textAbstract:
Sentiment is a high-level abstraction, and it is a challenging task to accurately extract sentimental features from visual contents due to the “affective gap”. Previous works focus on extracting more concrete sentimental features of individual objects by introducing saliency detection or instance segmentation into their models, neglecting the interaction among objects. Inspired by the observation that interaction among objects can impact the sentiment of images, we propose the Sentiment Interaction Distillation (SID) Network, which utilizes object sentimental interaction to guide feature learning. Specifically, we first utilize a panoptic segmentation method to obtain objects in images; then, we propose a sentiment-related edge generation method and employ Graph Convolution Network to aggregate and propagate object relation representation. In addition, we propose a knowledge distillation framework to utilize interaction information guiding global context feature learning, which can avoid noisy features introduced by error propagation and a varying number of objects. Experimental results show that our method outperforms the state-of-the-art algorithm, e.g., about 1.2% improvement on the Flickr dataset and 1.7% on the most challenging subset of Twitter I. It is demonstrated that the reasonable use of interaction features can improve the performance of sentiment analysis.
24
Wang, Cheng, Sirui Huang, and Ya Zhou. "Sentiment analysis of MOOC reviews via ALBERT-BiLSTM model." MATEC Web of Conferences 336 (2021): 05008. http://dx.doi.org/10.1051/matecconf/202133605008.
Full textAbstract:
The accurate exploration of the sentiment information in comments for Massive Open Online Courses (MOOC) courses plays an important role in improving its curricular quality and promoting MOOC platform’s sustainable development. At present, most of the sentiment analyses of comments for MOOC courses are actually studies in the extensive sense, while relatively less attention is paid to such intensive issues as the polysemous word and the familiar word with an upgraded significance, which results in a low accuracy rate of the sentiment analysis model that is used to identify the genuine sentiment tendency of course comments. For this reason, this paper proposed an ALBERT-BiLSTM model for sentiment analysis of comments for MOOC courses. Firstly, ALBERT was used to dynamically generate word vectors. Secondly, the contextual feature vectors were obtained through BiLSTM pre-sequence and post-sequence, and the attention mechanism that could calculate the weight of different words in a sentence was applied together. Finally, the BiLSTM output vectors were input into Softmax for the classification of sentiments and prediction of the sentimental tendency. The experiment was performed based on the genuine data set of comments for MOOC courses. It was proved in the result that the proposed model was higher in accuracy rate than the already existing models.
25
Mohtarami, Mitra, Hadi Amiri, Man Lan, Thanh Phu Tran, and Chew Lim Tan. "Sense Sentiment Similarity: An Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1706–12. http://dx.doi.org/10.1609/aaai.v26i1.8356.
Full textAbstract:
This paper describes an emotion-based approach to acquire sentiment similarity of word pairs with respect to their senses. Sentiment similarity indicates the similarity between two words from their underlying sentiments. Our approach is built on a model which maps from senses of words to vectors of twelve basic emotions. The emotional vectors are used to measure the sentiment similarity of word pairs. We show the utility of measuring sentiment similarity in two main natural language processing tasks, namely, indirect yes/no question answer pairs (IQAP) Inference and sentiment orientation (SO) prediction. Extensive experiments demonstrate that our approach can effectively capture the sentiment similarity of word pairs and utilize this information to address the above mentioned tasks.
26
Saxena, Surbhi, Anant Deogaonkar, Rupesh Pais, and Reshma Pais. "Workplace Productivity Through Employee Sentiment Analysis Using Machine Learning." International Journal of Professional Business Review 8, no. 4 (April 18, 2023): e01216. http://dx.doi.org/10.26668/businessreview/2023.v8i4.1216.
Full textAbstract:
Purpose: The objective of this study was to analyze workplace productivity through employee sentiment analysis using machine learning. Theoretical framework: A lot of literature is already published on employee productivity and sentiment analysis as a tool, but the study here is intended to address the issues in employee productivity post-COVID’19. Design/methodology/approach: The authors have studied the relationship between sentiments and workplace productivity post-COVID- 19. Sentiments were captured from the text inputs given by seventy-two survey respondents from a mid-sized consultancy firm and correlated against the productivity scores. A machine learning model was developed using Python to calculate the sentiment score. Findings: 98.6% of the respondents had a high productivity score, whereas 88.9% showed positive sentiments. The majority of the responses showed a positive correlation between positive sentiments and high productivity levels. Research, Practical and Social Implications: The study paves way for identification of action plan for productivity enhancement through sentiment analysis. Originality/Value: No previous work on employee productivity using sentiment analysis is done till now.
27
Mehta, Niyati, Minakshi Mane, Prathamesh Satpute, Aishwarya Mahale, and Shivshankar Bhutekar. "Sentiment Analysis for E-Commerce." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 2891–95. http://dx.doi.org/10.22214/ijraset.2023.50834.
Full textAbstract:
Abstract: The rise of e-commerce has brought about new challenges in understanding customers; needs and preferences. One of the ways to better understand customers is to analyse their feedback on products and services. Sentiment analysis is a powerful tool that can be used to gain insights into customers&; opinions and emotions towards products and services. The proposed project, "Happy Shopping" is an application that uses sentiment analysis to analyse customer feedback on products and services. The interface will display a summary of the most common sentiments expressed by customers for each product or service. This will enable customers to make more informed buying decisions based on the sentiments expressed by other customers. In conclusion, the "Happy Shopping" project aims to leverage sentiment analysis to enhance the customer experience in an e-commerce platform. The project will provide customers with valuable insights into other customers; sentiments towards products and services, as well as improve customer support services.
28
C, Ms DEVADHARSHINI. "Sentiment Analysis for Movie Recommendation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 12, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30356.
Full textAbstract:
Our research proposes a movie recommendation system integrating sentiment analysis with LSTM neural networks. By extracting sentiments from user reviews, LSTM captures nuanced preferences, enhancing recommendation accuracy. Leveraging real-world movie datasets, our approach outperforms existing methods, offering personalized recommendations aligned with user sentiments. Through this fusion of NLP and deep learning, we strive to streamline movie selection, providing users with a more tailored and satisfying experience. Keywords—Sentiment Analysis, Natural Language Processing,, Long Short-Term Memory, Recurrent Neural Network
29
Monika, Mrs Gyara, Sanjana Burra, Vungurala Divya, and Seelam Aswanth kumar. "Aspect-based Sentimental Analysis for Movie Recommendation." International Scientific Journal of Engineering and Management 04, no. 01 (January 30, 2025): 1–6. https://doi.org/10.55041/isjem02242.
Full textAbstract:
Aspect-based sentiment analysis (ABSA) is an advanced methodology in natural language processing (NLP) aimed at extracting and categorizing sentiments expressed in user reviews, specifically focusing on particular aspects of the subject. In the context of movie recommendations, ABSA facilitates a more nuanced understanding of audience preferences by analyzing reviews for targeted attributes like plot, acting, and cinematography. This study explores the application of ABSA in developing a movie recommendation system, leveraging its ability to extract and analyze aspect-level sentiments. We propose a model integrating state-of-the-art NLP techniques and sentiment analysis frameworks to optimize recommendation accuracy. Keyword: Aspect-based sentiment analysis, movie recommendation, natural language processing (NLP), sentiment analysis frameworks
30
H M, Dr Keerthi. "Review on Sentimental Analysis and Aspect Analysis with Codemix using LLM, BERT, and Naive Bayes." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (January 8, 2025): 1–9. https://doi.org/10.55041/ijsrem40598.
Full textAbstract:
In the era of social media, where multilingual conversations are prevalent, analyzing code-mixed text poses unique challenges. This project presents a comparative analysis of sentiment analysis and aspect-based sentiment analysis on code-mixed data using advanced techniques like Large Language Models (LLM), BERT, and Naive Bayes. Sentiment analysis categorizes text into positive, negative, or neutral sentiments, while aspect-based analysis identifies opinions on specific topics, such as "price" or "quality" in reviews. By focusing on code-mixed text, this study compares the effectiveness of each method in understanding sentiments and specific opinions, paving the way for improved applications in multilingual settings.
31
Khanvilkar, Gayatri, and Prof Deepali Vora. "Sentiment Analysis for Product Recommendation Using Random Forest." International Journal of Engineering & Technology 7, no. 3.3 (June 21, 2018): 87. http://dx.doi.org/10.14419/ijet.v7i3.3.14492.
Full textAbstract:
Analysis of sentiments is to analyze the natural language and to find the emotions, express by the human beings. The idea behind sentiment analysis is to determine polarity of textual opinion given by person. Sentiment Analysis is useful in product recommendations. Based on the reviews given by the user; the products can be recommended to another user. Major product websites are using sentiment analysis to understand the popularity and problems with the product. Sentiment analysis mainly formulated as two class classification problem, positive and negative. Sentiment analysis using ordinal classification gives more clear idea about sentiments. The proposed system determines polarity of reviews given by users, using ordinal classification. The system will give polarity using machine learning algorithms SVM and Random Forest. The achieved polarity will be used to provide recommendation to users.
32
Listiyono, Hersatoto, Zuly Budiarso, Susi Susilowati, and Agus Perdana Windarto. "Comprehensive Sentiment Analysis of Religious Content Naive Bayes Algorithm Model." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 1 (January 31, 2024): 602. http://dx.doi.org/10.30865/mib.v8i1.7062.
Full textAbstract:
This paper delves into sentiment analysis of online religious content utilizing the Naive Bayes algorithm to decipher the array of sentiments present in religious discussions. By tailoring this algorithm to the complexities of religious language, the study reveals hidden sentiments, offering valuable insights for researchers, policymakers, and communities. The findings demonstrate that the sentiment analysis model performs robustly, with a precision of 84.78%, a recall of 82.98%, and a balanced F1 Score of 83.87%, indicating high accuracy in sentiment identification and effectiveness in capturing a significant portion of actual sentiments. The overall accuracy of the model stands at 75.10%, affirming its successful adaptation to the intricacies of religious discourse. These results not only deepen our understanding of sentiment analysis in the realm of faith and spirituality but also have practical implications for enhancing interfaith dialogue, fostering mutual understanding, and guiding decision-making in religious and social organizations. This research makes a significant contribution to the growing field of sentiment analysis, providing a methodological framework for exploring the nuanced sentiment landscape within the domain of faith and spirituality.
33
Tan, Kian Long, Chin Poo Lee, and Kian Ming Lim. "A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research." Applied Sciences 13, no. 7 (April 3, 2023): 4550. http://dx.doi.org/10.3390/app13074550.
Full textAbstract:
Sentiment analysis is a critical subfield of natural language processing that focuses on categorizing text into three primary sentiments: positive, negative, and neutral. With the proliferation of online platforms where individuals can openly express their opinions and perspectives, it has become increasingly crucial for organizations to comprehend the underlying sentiments behind these opinions to make informed decisions. By comprehending the sentiments behind customers’ opinions and attitudes towards products and services, companies can improve customer satisfaction, increase brand reputation, and ultimately increase revenue. Additionally, sentiment analysis can be applied to political analysis to understand public opinion toward political parties, candidates, and policies. Sentiment analysis can also be used in the financial industry to analyze news articles and social media posts to predict stock prices and identify potential investment opportunities. This paper offers an overview of the latest advancements in sentiment analysis, including preprocessing techniques, feature extraction methods, classification techniques, widely used datasets, and experimental results. Furthermore, this paper delves into the challenges posed by sentiment analysis datasets and discusses some limitations and future research prospects of sentiment analysis. Given the importance of sentiment analysis, this paper provides valuable insights into the current state of the field and serves as a valuable resource for both researchers and practitioners. The information presented in this paper can inform stakeholders about the latest advancements in sentiment analysis and guide future research in the field.
34
Rawal, Ms Purvi, and Ms Anisha Asirvatham. "Sentimental Analysis on Twitter: Insights from Supervised Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (September 1, 2024): 1–13. http://dx.doi.org/10.55041/ijsrem37307.
Full textAbstract:
Sentimental analysis is use for analyzing the text so that it can determine theemotions what is in the message which can be positive, negative, or neutral. Social media platforms such as twitter, Instagram, Facebook, etc. where public opinions have been expressed a lot. Other sentimental analysis algorithms include the rules-based and hybrid approach for data processing. Sentiment algorithms are also analysed accordingly with in forms of text like sentences, It also contains relevantinterpretations through the information that was provided. In this study, a baseline model was first established, achieving an accuracy to serve as a comparison point for more sophisticated models. The Logistic Regression model outperformed the baseline significantly, demonstrating its effectiveness in accurately classifying sentiments. The Decision Tree Classification model, while an improvement over the baseline was less accurate than Logistic Regression, suggesting potential issues with overfitting and data dependency. The Random Forest Classification model provided a robust alternative, matching the performance of Logistic Regression and benefiting from the ensemble approach to handle diverse patterns in the sentiment data. Keywords: Sentimental analysis, Machine learning, supervised learning, twitter
35
Sudirman, Ivan, Iman Sudirman, Margareth Setiawan, and Intan Rahmatillah. "TRANSFORMING PRODUCT INNOVATION TO MEET CUSTOMER NEEDS THROUGH AI MARKETING, A CUSTOMER FEEDBACK ANALYSIS WITH GPT-4O MINI." Jurnal Riset Bisnis dan Manajemen 18, no. 1 (February 21, 2025): 15–26. https://doi.org/10.23969/jrbm.v18i1.18021.
Full textAbstract:
This research uses GPT to conduct sentiment analysis on customer reviews for biodegradable products. Sentiment analysis uses 4 categories, namely positive, negative, neutral and mixed, then for product improvement this study focuses on negative sentiment by adding negative sentiments from the mixed sentiments. Data collected from Amazon reviews regarding one brand of biodegradable trash bag in several stores. The GPT-4o Mini model was then used to categorize sentiment.The results of sentiment analysis show that most reviews are positive, but there are also many negative sentiments regarding product durability, leakage and price. The model used is able to accurately identify and extract negative sentiment even from a mixed sentiment, thereby providing a more complete understanding of customer dissatisfaction. This research emphasizes the importance of integrating AI-driver sentiment analysis into the marketing process.
36
Gnanapriya, Dr S. Gnanapriya, and R. Arun Kumar. "Fraud App Detection Using Sentiment Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (March 14, 2025): 1–9. https://doi.org/10.55041/ijsrem42480.
Full textAbstract:
Since there are more and more mobile applications used in daily life, it's critical to monitor which ones are secure and which are not. Based just on the reviews listed for each program, one cannot determine how reliable and safe each one is. Therefore, it is essential to verify and start a system to ensure that the apps are authentic or fraudulent. The goal is to create a web system that uses support vector machines and sentiment analysis to identify fraudulent apps before users download them. The purpose of sentiment analysis is to assist in identifying the emotional undertones of words used in online communication. This approach is helpful for keeping an eye on social media and for quickly gauging public sentiment on particular topics. On the internet, the customer may not always find accurate or genuine product reviews. The reviews could be authentic or fraudulent. We can ascertain whether or not the app is authentic by examining evaluations that include remarks from both users and administrators. The system can learn and understand the sentiments and emotions of reviews and other materials by using support vector machines and sentimental analysis. One of the main components of app ranking fraud is the manipulation of reviews. The right app for iOS and Android can be found by examining reviews and comments using emotional analysis and support vector machines. Keywords : Positive negative neutral reviews, Sentiment analysis, Support Vector Machine, Users reviews
37
Ashwini Bari, Rajeev Yadav. "Analysis of Product Review Sentiment Analysis using Improved Machine Learning Techniques." Kuwait Journal of Computer Science 1, no. 1 (March 31, 2023): 30–37. http://dx.doi.org/10.52783/kjcs.v1i1.232.
Full textAbstract:
Sentiment analysis has emerged as a crucial task in the era of big data and social media. Understanding the sentiments expressed in product reviews is vital for businesses to gauge customer satisfaction and make informed decisions. This research paper presents a design simulation and assessment of product review sentiment analysis using improved machine learning techniques. The aim is to develop a robust sentiment analysis model that outperforms existing approaches in accuracy and efficiency. We propose a novel methodology that combines advanced feature extraction, sentiment classification algorithms, and model optimization techniques. The introduction provides an overview of the importance of sentiment analysis in the context of product reviews and the challenges faced by conventional methods. It also outlines the objectives and scope of this research. The related works section presents a comprehensive review of existing literature and highlights the limitations of current approaches. The proposed methodology section describes the technical details of our enhanced machine learning approach and the reasoning behind the selected techniques. In the analysis of sample results, we evaluate the performance of our proposed model on a diverse dataset of product reviews. We present the accuracy, precision, recall, and F1-score metrics, along with a comparison to baseline models and state-of-the-art sentiment analysis systems. Furthermore, we discuss the model's robustness in handling various types of products and reviews.
Our research demonstrates significant improvements in sentiment analysis accuracy compared to traditional methods. We introduce tables and graphs to illustrate the model's performance in different scenarios and identify its strengths and weaknesses. The paper concludes by discussing the implications of our findings, potential applications in industry, and directions for future research. Overall, this research contributes to the advancement of sentiment analysis techniques and provides a valuable resource for businesses aiming to enhance their understanding of customer sentiments through product reviews.
38
Merlin Durairaj John Louis, Anitha, and Vimal Kumar Dhanasekaran. "SentimentLP: unveiling advanced sentiment analysis through Leptotila optimization-based gradient boosting machines." Bulletin of Electrical Engineering and Informatics 14, no. 2 (April 1, 2025): 1212–22. https://doi.org/10.11591/eei.v14i2.8959.
Full textAbstract:
Sentiment analysis is pivotal in extracting insights from textual data, enabling organizations to understand customer opinions, market trends, and brand perception. This study introduces a novel approach, SentimentLP, which integrates Leptotila optimization (LPO) with gradient boosting machines (GBM) for sentiment analysis tasks. The proposed framework leverages LPO’s dynamic optimization capabilities to enhance GBM models’ performance in sentiment classification. Through iterative refinement and adaptive learning, SentimentLP optimizes feature extraction, model training, and ensemble learning processes, improving sentiment analysis accuracy and efficiency. Results from various evaluation metrics, including precision, recall, classification accuracy, and F-measure, demonstrate the effectiveness of SentimentLP in accurately capturing sentiment expressions in text data. Additionally, the fusion of LPO with GBM ensures scalability, adaptability, and interpretability of sentiment analysis models, making SentimentLP a valuable tool for extracting actionable insights from textual data across diverse domains and applications.
39
Sharma Vishalkumar Sureshbhai and Dr. Tulsidas Nakrani. "A Literature Review : Enhancing Sentiment Analysis of Deep Learning Techniques Using Generative AI Model." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (June 15, 2024): 530–40. http://dx.doi.org/10.32628/cseit24103204.
Full textAbstract:
Sentiment analysis is possibly one of the most desirable areas of study within Natural Language Processing (NLP). Generative AI can be used in sentiment analysis through the generation of text that reflects the sentiment or emotional tone of a given input. The process typically involves training a generative AI model on a large dataset of text examples labeled with sentiments (positive, negative, neutral, etc.). Once trained, the model can generate new text based on the learned patterns, providing an automated way to analyze sentiments in user reviews, comments, or any other form of textual data. The main goal of this research topic is to identify the emotions as well as opinions of users or customers using textual means. Though a lot of research has been done in this area using a variety of models, sentiment analysis is still regarded as a difficult topic with a lot of unresolved issues. Slang terms, novel languages, grammatical and spelling errors, etc. are some of the current issues. This work aims to conduct a review of the literature by utilizing multiple deep learning methods on a range of data sets. Nearly 21 contributions, covering a variety of sentimental analysis applications, are surveyed in the current literature study. Initially, the analysis looks at the kinds of deep learning algorithms that are being utilized and tries to show the contributions of each work. Additionally, the research focuses on identifying the kind of data that was used. Additionally, each work's performance metrics and setting are assessed, and the conclusion includes appropriate research gaps and challenges. This will help in identifying the non-saturated application for which sentimental analysis is most needed in future studies.
40
Singh, Pradeep. "Near Real-time Sentiment Analysis using ChatGPT." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (June 30, 2024): 706–9. http://dx.doi.org/10.22214/ijraset.2024.63216.
Full textAbstract:
Abstract: Sentiment analysis, also known as opinion mining, analyses people's opinions, sentiments, attitudes, and emotions from written language. With the rapid growth of social media and other real-time communication platforms, the demand for real-time sentiment analysis has surged. This paper explores the application of OpenAI's ChatGPT, a state-of-the-art language model, in conducting near real-time sentiment analysis. The study investigates the model's capabilities, performance, and potential limitations, proposing a framework for integrating ChatGPT into real-time sentiment analysis systems.
41
M, Vasuki. "Enhancing Social Media Insights: Leveraging Artificial Intelligence for Sentiment Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 16, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33351.
Full textAbstract:
Social media platforms have become indispensable channels for communication and interaction, offering a wealth of data that can provide valuable insights into public sentiments. However, analyzing sentiment on these platforms poses significant challenges due to the diverse and unstructured nature of user-generated content. Traditional Natural Language Processing (NLP) techniques struggle to accurately classify sentiments expressed through text, images, emoticons, and multimedia elements. Moreover, the informal and nuanced language used in Electronic Word of Mouth (eWOM) further complicates sentiment analysis. In response, this paper explores the role of Artificial Intelligence (AI) in improving sentiment analysis on social media. By leveraging Machine Learning (ML) algorithms trained on large datasets, AI can enhance the accuracy and efficiency of sentiment classification, providing decision-makers with actionable insights into the sentiment landscape of social media. Keywords: Social media, Sentiment analysis, Artificial Intelligence, Machine Learning, Natural Language Processing, Electronic Word of Mouth.
42
Huang, Changqin, Zhongmei Han, Ming Li, Xizhe Wang, and Wenzhu Zhao. "Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis." Australasian Journal of Educational Technology 37, no. 2 (May 10, 2021): 81–95. http://dx.doi.org/10.14742/ajet.6749.
Full textAbstract:
Sentiment evolution is a key component of interactions in blended learning. Although interactions have attracted considerable attention in online learning contexts, there is scant research on examining sentiment evolution over different interactions in blended learning environments. Thus, in this study, sentiment evolution at different interaction levels was investigated from the longitudinal data of five learning stages of 38 postgraduate students in a blended learning course. Specifically, text mining techniques were employed to mine the sentiments in different interactions, and then epistemic network analysis (ENA) was used to uncover sentiment changes in the five learning stages of blended learning. The findings suggested that negative sentiments were moderately associated with several other sentiments such as joking, confused, and neutral sentiments in blended learning contexts. Particularly in relation to deep interactions, student sentiments might change from negative to insightful ones. In contrast, the sentiment network built from social-emotion interactions shows stronger connections in joking-positive and joking-negative sentiments than the other two interaction levels. Most notably, the changes of co-occurrence sentiment reveal the three periods in a blended learning process, namely initial, collision and sublimation, and stable periods. The results in this study revealed that students’ sentiments evolved from positive to confused/negative to insightful.
43
An, Ruopeng, Yuyi Yang, Quinlan Batcheller, and Qianzi Zhou. "Sentiment Analysis of Tweets on Soda Taxes." Journal of Public Health Management and Practice 29, no. 5 (February 20, 2023): 633–39. http://dx.doi.org/10.1097/phh.0000000000001721.
Full textAbstract:
Context: As a primary source of added sugars, sugar-sweetened beverage (SSB) consumption may contribute to the obesity epidemic. A soda tax is an excise tax charged on selling SSBs to reduce consumption. Currently, 8 cities/counties in the United States have imposed soda taxes. Objective: This study assessed people's sentiments toward soda taxes in the United States based on social media posts on Twitter. Design: We designed a search algorithm to systematically identify and collect soda tax–related tweets posted on Twitter. We built deep neural network models to classify tweets by sentiments. Setting: Computer modeling. Participants: Approximately 370 000 soda tax–related tweets posted on Twitter from January 1, 2015, to April 16, 2022. Main Outcome Measure: Sentiment associated with a tweet. Results: Public attention paid to soda taxes, indicated by the number of tweets posted annually, peaked in 2016, but has declined considerably ever since. The decreasing prevalence of tweets quoting soda tax–related news without revealing sentiments coincided with the rapid increase in tweets expressing a neutral sentiment toward soda taxes. The prevalence of tweets expressing a negative sentiment rose steadily from 2015 to 2019 and then slightly leveled off, whereas that of tweets expressing a positive sentiment remained unchanged. Excluding news-quoting tweets, tweets with neutral, negative, and positive sentiments occupied roughly 56%, 29%, and 15%, respectively, during 2015-2022. The authors' total number of tweets posted, followers, and retweets predicted tweet sentiment. The finalized neural network model achieved an accuracy of 88% and an F1 score of 0.87 in predicting tweet sentiments in the test set. Conclusions: Despite its potential to shape public opinion and catalyze social changes, social media remains an underutilized source of information to inform government decision making. Social media sentiment analysis may inform the design, implementation, and modification of soda tax policies to gain social support while minimizing confusion and misinterpretation.
44
Cardoso Durier da Silva, Fernando, Ana Cristina Bicharra Garcia, and Sean Wolfgand Matsui Siqueira. "Sentiment Gradient - Improving Sentiment Analysis with Entropy Increase." Inteligencia Artificial 26, no. 71 (May 12, 2023): 114–30. http://dx.doi.org/10.4114/intartif.vol26iss71pp114-130.
Full textAbstract:
Information sharing on the Web has also led to the rise and spread of fake news. Considering that fake information is generally written to trigger stronger feelings from the readers than simple facts, sentiment analysis has been widely used to detect fake news. Nevertheless, sarcasm, irony, and even jokes use similarwritten styles, making the distinction between fake and fact harder to catch automatically. We propose a new fake news Classifier that considers a set of language attributes and the gradient of sentiments contained in a message. Sentiment analysis approaches are based on labelling news with a unique value that shrinks the entire message to a single feeling. We take a broader view of a message’s sentiment representation, trying to unravel the gradient of sentiments a message may bring. We tested our approach using two datasets containing texts written in Portuguese: a public one and another we created with more up-to-date news scrapped from the Internet. Although we believe our approach is general, we tested for the Portuguese language. Our results show that the sentiment gradient positively impacts the fake news classification performance with statistical significance. The F-Measure reached 94 %, with our approach surpassing available ones (with a p-value less than 0.05 for our results).
45
Thelwall, Mike. "Gender bias in sentiment analysis." Online Information Review 42, no. 1 (February 12, 2018): 45–57. http://dx.doi.org/10.1108/oir-05-2017-0139.
Full textAbstract:
Purpose The purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females. Design/methodology/approach This paper uses data sets of TripAdvisor reviews of hotels and restaurants in the UK written by UK residents to contrast the accuracy of lexical sentiment analysis for males and females. Findings Male sentiment is harder to detect because it is less explicit. There was no evidence that this problem could be solved by gender-specific lexical sentiment analysis. Research limitations/implications Only one lexical sentiment analysis algorithm was used. Practical implications Care should be taken when drawing conclusions about gender differences from automatic sentiment analysis results. When comparing opinions for product aspects that appeal differently to men and women, female sentiments are likely to be overrepresented, biasing the results. Originality/value This is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another.
46
Samih, Amina, Abderrahim Ghadi, and Abdelhadi Fennan. "Enhanced sentiment analysis based on improved word embeddings and XGboost." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (April 1, 2023): 1827. http://dx.doi.org/10.11591/ijece.v13i2.pp1827-1836.
Full textAbstract:
<span lang="EN-US">Sentiment analysis is a well-known and rapidly expanding study topic in natural language processing (NLP) and text classification. This approach has evolved into a critical component of many applications, including politics, business, advertising, and marketing. Most current research focuses on obtaining sentiment features through lexical and syntactic analysis. Word embeddings explicitly express these characteristics. This article proposes a novel method, improved words vector for sentiments analysis (IWVS), using XGboost to improve the F1-score of sentiment classification. The proposed method constructed sentiment vectors by averaging the word embeddings (Sentiment2Vec). We also investigated the Polarized lexicon for classifying positive and negative sentiments. The sentiment vectors formed a feature space to which the examined sentiment text was mapped to. Those features were input into the chosen classifier (XGboost). We compared the F1-score of sentiment classification using our method via different machine learning models and sentiment datasets. We compare the quality of our proposition to that of baseline models, term frequency-inverse document frequency (TF-IDF) and Doc2vec, and the results show that IWVS performs better on the F1-measure for sentiment classification. At the same time, XGBoost with IWVS features was the best model in our evaluation.</span>
47
Ghatora, Pawanjit Singh, Seyed Ebrahim Hosseini, Shahbaz Pervez, Muhammad Javed Iqbal, and Nabil Shaukat. "Sentiment Analysis of Product Reviews Using Machine Learning and Pre-Trained LLM." Big Data and Cognitive Computing 8, no. 12 (December 23, 2024): 199. https://doi.org/10.3390/bdcc8120199.
Full textAbstract:
Sentiment analysis via artificial intelligence, i.e., machine learning and large language models (LLMs), is a pivotal tool that classifies sentiments within texts as positive, negative, or neutral. It enables computers to automatically detect and interpret emotions from textual data, covering a spectrum of feelings without direct human intervention. Sentiment analysis is integral to marketing research, helping to gauge consumer emotions and opinions across various sectors. Its applications span analyzing movie reviews, monitoring social media, evaluating product feedback, assessing employee sentiments, and identifying hate speech. This study explores the application of both traditional machine learning and pre-trained LLMs for automated sentiment analysis of customer product reviews. The motivation behind this work lies in the demand for more nuanced understanding of consumer sentiments that can drive data-informed business decisions. In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. Traditional models show better results and efficiency in processing short, concise text, with SVM in classifying sentiment of short length comments. However, GPT-4 showed better results with more detailed texts, capturing subtle sentiments with higher precision, recall, and F1 scores to uniquely identify mixed sentiments not found in the simpler models. Conclusively, this study shows that LLMs outperform traditional models in context-rich sentiment analysis by not only providing accurate sentiment classification but also insightful explanations. These results enable LLMs to provide a superior tool for customer-centric businesses, which helps actionable insights to be derived from any textual data.
48
Srinidhi, K., T. L.S Tejaswi, CH Rama Rupesh Kumar, and I. Sai Siva Charan. "An Advanced Sentiment Embeddings with Applications to Sentiment Based Result Analysis." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 393. http://dx.doi.org/10.14419/ijet.v7i2.32.15721.
Full textAbstract:
We propose an advanced well-trained sentiment analysis based adoptive analysis “word specific embedding’s, dubbed sentiment embedding’s”. Using available word and phrase embedded learning and trained algorithms mainly make use of contexts of terms but ignore the sentiment of texts and analyzing the process of word and text classifications. sentimental analysis on unlike words conveying same meaning matched to corresponding word vector. This problem is bridged by combining encoding opinion carrying text with sentiment embeddings words. But performing sentimental analysis on e-commerce, social networking sites we developed neural network based algorithms along with tailoring and loss function which carry feelings. This research apply embedding’s to word-level, sentence-level sentimental analysis and classification, constructing sentiment oriented lexicons. Experimental analysis and results addresses that sentiment embedding techniques outperform the context-based embedding’s on many distributed data sets. This work provides familiarity about neural networks techniques for learning word embedding’s in other NLP tasks.
49
Suryatin, Suryatin, Dhomas Hatta Fudholi, Chandra Kusuma Dewa, and Nur Iman. "Aspect-Based Sentiment Analysis Pada Aplikasi Pelacakan Kasus Covid-19 (Studi Kasus: Pedulilindungi)." SIMKOM 9, no. 1 (January 14, 2024): 12–22. http://dx.doi.org/10.51717/simkom.v9i1.304.
Full textAbstract:
Berbagi pengalaman melalui internet dan media sosial dapat menunjukan sikap dan perasaan dalam bentuk umpan balik. Aplikasi publik yang banyak disoroti pada masa wabah corona virus yaitu aplikasi pedulilindungi yang merupakan aplikasi monitoring perkembangan Corona Virus Disease di Indonesia. Salah satu fenomena timbulnya Aspect-based sentiment dalam pada prilaku sentimentil masyarakat terhadap layanan aplikasi pedulilindungi. Penelitian ini bertujuan untuk mengetahui besarnya nilai sentimen pada layanan pedulilindungi dan berfokus pada aspect based sentiment analysis (ABSA) pada domain ulasan aplikasi pemerintah. Analisis terdiri dari user interface, user experience, fungsionalitas dan work scurity. Metode yang digunakan meliputi klasifikasi sentimen dan aspek dengan metode deep learning (CNN,GRU dan TCN). Data primer bersumber dari hasil ulasan aplikasi pedulilindungi dengan teknik scraping pada situs https://www.pedulilindungi.id/. Hasil penelitian menunjukan bahwa terdapat enam aspek klasisifikasi sentimen pada aplikasi pedulilindungi yaitu aplikasi, user interface, user experience, kode OTP, cek sertifikat vaksin, bukti akses layanan. Hasil penelitian juga menunjukan bahwa metode CNN memperoleh nilai skor akurasi terbaik pada klasifikasi sentimen sebesar 98% dan klasifikasi aspek sebesar 97%.
50
Alsaeedi, Riyadh. "Sentiment Analysis of Arabic Tweets: Detecting Revilement." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 15, no. 3 (October 14, 2024): 312–23. http://dx.doi.org/10.61841/turcomat.v15i3.14726.
Full textAbstract:
Social media systems play an necessary function in shaping public opinion and reflecting societal sentiments. This study focuses on sentiment analysis in Arabic tweets with a particular focus on offensive or offensive content. The aim of this research is to boost a dependable sentiment evaluation model that can accurately classify Arabic tweets as positive or negative, with a particular focus on identifying offensive language. A multinomial Naive Bayes classifier is trained on pre-processed data to perform sentiment classification. The classifier is fine-tuned to differentiate between positive and negative emotions, with a particular focus on identifying offensive or swearing language. The model is evaluated the usage of a complete set of metrics along with precision, precision, recall, and F1score. Experimental consequences point out promising overall performance of the developed sentiment evaluation model. The model achieved an accuracy of 93%, effectively classifying Arabic tweets into effective and bad sentiments. The precision, recall, and F1-score metrics similarly validate the model's capacity to precisely become aware of revilement and offensive language. These outcomes spotlight the conceivable of the proposed strategy in successfully examining Arabic tweets for sentiment and offensive content, contributing to higher grasp on line behaviors and sentiments in the context of revilement.