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Journal articles on the topic 'Machine Learning Semantic Orientation Sentiment Analysis Twitter'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

G.K, Madhura, and Puneet Shetteppanavar. "TWITTER SENTIMENT ANALYSIS FOR PRODUCT REVIEWS TO GATHER INFORMATION USING MACHINE LEARNING TECHNIQUE." International Journal of Advanced Research 10, no. 03 (2022): 669–74. http://dx.doi.org/10.21474/ijar01/14435.

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The concept of sentiment analysis of twitter data and semantic analysis with the augmentation of machine learning methodologies has become a hot topic in recent years. Many strategies have been presented in the area of sentiment analysis in the last few years to evaluate social media data and produce a graphical presentation towards a certain business. Sentiment analysis shows you how people feel about a product or brand when penning a social media message about it. This is crucial information if you know that one persons opinion of a firm or its products might impact the opinions of others. L

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Aldayel, Haifa K., and Aqil M. Azmi. "Arabic tweets sentiment analysis – a hybrid scheme." Journal of Information Science 42, no. 6 (2016): 782–97. http://dx.doi.org/10.1177/0165551515610513.

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The fact that people freely express their opinions and ideas in no more than 140 characters makes Twitter one of the most prevalent social networking websites in the world. Being popular in Saudi Arabia, we believe that tweets are a good source to capture the public’s sentiment, especially since the country is in a fractious region. Going over the challenges and the difficulties that the Arabic tweets present – using Saudi Arabia as a basis – we propose our solution. A typical problem is the practice of tweeting in dialectical Arabic. Based on our observation we recommend a hybrid approach tha

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Ali, Syed Fahad, and Nayyer Masood. "Evaluation of adjective and adverb types for effective Twitter sentiment classification." PLOS ONE 19, no. 5 (2024): e0302423. http://dx.doi.org/10.1371/journal.pone.0302423.

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Twitter, the largest microblogging platform, has reported more than 330 million active users in recent years. Many users express their sentiments about politics, sports, products, personalities, etc. Sentiment analysis has emerged as a specialized branch of machine learning in which tweets are binary-classified to provide sentimental insights. A major step in sentiment classification is feature selection, which primarily revolves around parts of speech (POS). Few techniques merely focused on single features such as adjectives, adverbs, and verbs, while other techniques examined types of these

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Rani, Meesala Shobha, and Sumathy S. "Perspectives of the performance metrics in lexicon and hybrid based approaches: a review." International Journal of Engineering & Technology 6, no. 4 (2017): 108. http://dx.doi.org/10.14419/ijet.v6i4.8295.

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Online social media and social networking services experience a drastic development in the present scenario. Contents generated by hundreds of millions of users are used for communication in general. Users mark their opinion and review in various applications such as Twitter, Facebook, YouTube, Weibo, Flicker, LinkedIn, Online-e commerce sites, Microblogging sites, etc. User generated text is spread rapidly on the web, and it has become tedious to analyze the opinionated text in order to arrive at a decision. Sentiment analysis, a sub-category of text mining is the major active research domain

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Kumar, Anil`, Tanu Gupta, Dr Abhay Bhatia, and Rishav Raj. "TWITTER DATA SENTIMENT ANALYSIS FORSTOCK MARKET PREDICTION USING MACHINE LEARNING." International Journal of Engineering Applied Sciences and Technology 8, no. 5 (2023): 86–90. http://dx.doi.org/10.33564/ijeast.2023.v08i05.011.

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: Recent outrageous posts on social media have taken the globe by storm and have led to diverse views and views of the general public. Social media plays a significant act for or against a government or a corporation that simply can’t decide the movement of market but to grasp the sentiment of twitter data that are posted on social media with good method could be a supreme necessity. It will analyse some twitter postings to grasp human semantic. In any tweet intended posting there are some downgraded keyword. At last, a data-set is ready that consists of unique words collected from twitter pos

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Chandurkar, Toshita. "Sentiment Analysis of College Reviews using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1816–20. http://dx.doi.org/10.22214/ijraset.2021.35377.

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Sentiment analysis is the process of detecting positive or negative or neutral sentiment in text. It’s often used by businesses to detect sentiment in social data, gauge brand reputation, to understand it and make better. Sentiment analysis models focused on polarity (positive, negative and neutral) and even intentions (interested or not interested). Depending on how we wish want to interpret feedback and queries, we will define and tailor your categories to meet your sentiment analysis needs. This paper focuses the reviews of various colleges which are an important form of opinion mining. The

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Fattah, Mohammed, and Mohd Anul Haq. "Tweet Prediction for Social Media using Machine Learning." Engineering, Technology & Applied Science Research 14, no. 3 (2024): 14698–703. http://dx.doi.org/10.48084/etasr.7524.

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Tweet prediction plays a crucial role in sentiment analysis, trend forecasting, and user behavior analysis on social media platforms such as X (Twitter). This study delves into optimizing Machine Learning (ML) models for precise tweet prediction by capturing intricate dependencies and contextual nuances within tweets. Four prominent ML models, i.e. Logistic Regression (LR), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) were utilized for disaster-related tweet prediction. Our models adeptly discern semantic meanings, sentiment, and pertinent context from tweets, ensuring robust

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Lovera, Fernando Andres, Yudith Coromoto Cardinale, and Masun Nabhan Homsi. "Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification." Electronics 10, no. 22 (2021): 2739. http://dx.doi.org/10.3390/electronics10222739.

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The traditional way to address the problem of sentiment classification is based on machine learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge graphs give a way to extract structured knowledge from images and texts in order to facilitate their semantic analysis. This work proposes a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques to identify the sentiment polarity (positive or negative)

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Yenkikar, Anuradha, C. Narendra Babu, and D. Jude Hemanth. "Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble." PeerJ Computer Science 8 (September 20, 2022): e1100. http://dx.doi.org/10.7717/peerj-cs.1100.

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The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning

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Rifaldi, Dianda, Abdul Fadlil, and Herman. "Implementation of Word Trends Using a Machine Learning Approach with TF-IDF and Latent Dirichlet Allocation." JOIV : International Journal on Informatics Visualization 8, no. 4 (2024): 2297. https://doi.org/10.62527/joiv.8.4.2452.

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In today's technological age, the prevalence of social media has become ubiquitous, facilitating the easy dissemination of information and communication. This has led to the uploading of various content, including opinions on mental health, particularly in Indonesia. Mental health refers to an individual's emotional, psychological, and social well-being, commonly affecting individuals from adolescence to adulthood. This research utilized Twitter data on mental health issues gathered from October to November 2022, employing TF-IDF and Latent Dirichlet Allocation (LDA) to conduct topic modeling

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M, Patel, and Vikram R.V. "SENTIMENT ANALYSIS OF TWEETS USING DEEP LEARNING." ICTACT Journal on Data Science and Machine Learning 6, no. 1 (2024): 729–34. https://doi.org/10.21917/ijdsml.2024.0149.

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In the age of social media, Twitter has emerged as one of the largest platforms for expressing personal opinions and emotions. The vast volume of real-time data shared by users offers unique opportunities for analyzing public sentiment on various topics, including politics, entertainment, and social issues. However, extracting meaningful insights from such unstructured data presents significant challenges. Traditional sentiment analysis methods often struggle with the nuances of language, such as sarcasm, irony, and the context-dependent meaning of words. Thus, there is a need for more advance

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Mansour, Omar, Eman Aboelela, Remon Talaat, and Mahmoud Bustami. "Transformer-based ensemble model for dialectal Arabic sentiment classification." PeerJ Computer Science 11 (March 24, 2025): e2644. https://doi.org/10.7717/peerj-cs.2644.

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Social media platforms such as X, Facebook, and Instagram have become essential avenues for individuals to articulate their opinions, especially during global emergencies. These platforms offer valuable insights that necessitate analysis for informed decision-making and a deeper understanding of societal trends. Sentiment analysis is crucial for assessing public sentiment toward specific issues; however, applying it to dialectal Arabic presents considerable challenges in natural language processing. The complexity arises from the language’s intricate semantic and morphological structures, alon

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Sykora, Martin, Suzanne Elayan, and Thomas W. Jackson. "A qualitative analysis of sarcasm, irony and related #hashtags on Twitter." Big Data & Society 7, no. 2 (2020): 205395172097273. http://dx.doi.org/10.1177/2053951720972735.

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As the use of automated social media analysis tools surges, concerns over accuracy of analytics have increased. Some tentative evidence suggests that sarcasm alone could account for as much as a 50% drop in accuracy when automatically detecting sentiment. This paper assesses and outlines the prevalence of sarcastic and ironic language within social media posts. Several past studies proposed models for automatic sarcasm and irony detection for sentiment analysis; however, these approaches result in models trained on training data of highly questionable quality, with little qualitative appreciat

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Paredes-Valverde, Mario, Jorge Limon-Romero, Diego Tlapa, and Yolanda Baez-Lopez. "Sentiment Classification of Spanish Reviews: An Approach based on Feature Selection and Machine Learning Methods." JUCS - Journal of Universal Computer Science 22, no. (5) (2016): 691–708. https://doi.org/10.3217/jucs-022-05-0691.

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Sentiment analysis aims to extract users' opinions from review documents. Nowadays, there are two main approaches for sentiment analysis: the semantic orientation and the machine learning. Sentiment analysis approaches based on Machine Learning (ML) methods work over a set of features extracted from the users' opinions. However, the high dimensionality of the feature vector reduces the effectiveness of this approach. In this sense, we propose a sentiment classification method based on feature selection mechanisms and ML methods. The present method uses a hybrid feature extraction method based

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Bangyal, Waqas Haider, Rukhma Qasim, Najeeb ur Rehman, et al. "Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches." Computational and Mathematical Methods in Medicine 2021 (November 15, 2021): 1–14. http://dx.doi.org/10.1155/2021/5514220.

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A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; w

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Khan, Asif, Huaping Zhang, Jianyun Shang, et al. "Predicting Politician’s Supporters’ Network on Twitter Using Social Network Analysis and Semantic Analysis." Scientific Programming 2020 (September 1, 2020): 1–17. http://dx.doi.org/10.1155/2020/9353120.

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Politics is one of the hottest and most commonly mentioned and viewed topics on social media networks nowadays. Microblogging platforms like Twitter and Weibo are widely used by many politicians who have a huge number of followers and supporters on those platforms. It is essential to study the supporters’ network of political leaders because it can help in decision making when predicting their political futures. This study focuses on the supporters’ network of three famous political leaders of Pakistan, namely, Imran Khan (IK), Maryam Nawaz Sharif (MNS), and Bilawal Bhutto Zardari (BBZ). This

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Alhaizaey, Abdulrahim, and Jawad Berri. "Combining Machine Learning and Semantic Analysis for Efficient Misinformation Detection of Arabic Covid-19 Tweets." International Journal of Computer Science and Information Technology 14, no. 03 (2022): 31–40. http://dx.doi.org/10.5121/ijcsit.2022.14303.

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With the spread of social media platforms and the proliferation of misleading news, misinformation detection within microblogging platforms has become a real challenge. During the Covid-19 pandemic, many fake news and rumors were broadcasted and shared daily on social media. In order to filter out these fake news, many works have been done on misinformation detection using machine learning and sentiment analysis in the English language. However, misinformation detection research in the Arabic language on social media is limited. This paper introduces a misinformation verification system for Ar

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Maharani, Warih, and Veronikha Effendy. "Big five personality prediction based in Indonesian tweets using machine learning methods." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1973. http://dx.doi.org/10.11591/ijece.v12i2.pp1973-1981.

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<span lang="EN-US">The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including <a name="_Hlk87278444"></a>naiv

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Warih, Maharani, and Effendy Veronikha. "Big five personality prediction based in Indonesian tweets using machine learning methods." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1973–81. https://doi.org/10.11591/ijece.v12i2.pp1973-1981.

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The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including naive Bayes (NB), K-nearest neighbors (KNN), and support vecto

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Kusum and Panda Supriya. "Prediction of Election by Twitter." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 847–50. https://doi.org/10.35940/ijeat.C5284.029320.

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Nowadays social media like Twitter and Facebook etc. is one of the key players. Twitters are micro blogging sites by which users sent their opinions and views in brief. The information generated by one user can be seen by everyone. Therefore to analyze twitter sentiment can be a crucial task. For this task, we have used various approaches like novel based approach and machine learning and many other rules like context awareness are used for the detection of public opinion and prediction of results. We are studying the user tweets during elections. Meaningful tweets are collected on a definite

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Ploscă, Traian-Radu, Christian-Daniel Curiac, and Daniel-Ioan Curiac. "Investigating Semantic Differences in User-Generated Content by Cross-Domain Sentiment Analysis Means." Applied Sciences 14, no. 6 (2024): 2421. http://dx.doi.org/10.3390/app14062421.

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Sentiment analysis of domain-specific short messages (DSSMs) raises challenges due to their peculiar nature, which can often include field-specific terminology, jargon, and abbreviations. In this paper, we investigate the distinctive characteristics of user-generated content across multiple domains, with DSSMs serving as the central point. With cross-domain models on the rise, we examine the capability of the models to accurately interpret hidden meanings embedded in domain-specific terminology. For our investigation, we utilize three different community platform datasets: a Jira dataset for D

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AMARAVATHI PENTAGANTI and Dr. SONAKSHI KHURANA. "OPTIMIZING SOCIAL MEDIA SENTIMENT ANALYSIS USING HYBRID DATA MINING TECHNIQUES FOR ENHANCED MARKETING INTELLIGENCE." International Journal of Engineering Research and Science & Technology 13, no. 1 (2017): 19–33. https://doi.org/10.62643/ijerst.2017.v13.i1.pp19-33.

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The rapid growth of social networking platforms has created a vast digital landscape rich with consumer opinions, preferences, and behaviors. This thesis presents a hybrid approach to social media sentiment analysis, aiming to enhance marketing intelligence by integrating textual, contextual, and behavioral data mining techniques. Traditional sentiment classification methods, often based solely on text, fail to account for the dynamic nature of social platforms. In response, this research proposes a model that combines rule-based ensemble sentiment scoring with supervised machine learning and

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Ariyanto, Amelia Devi Putri, Fari Katul Fikriah, and Arif Fitra Setyawan. "Impact of Statistical and Semantic Features Extraction for Emotion Detection on Indonesian Short Text Sentences." CommIT (Communication and Information Technology) Journal 19, no. 1 (2025): 1–13. https://doi.org/10.21512/commit.v19i1.11680.

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The ability to detect emotions in short texts is crucial because interpreting emotions on platforms like Twitter can offer insight into social trends and responses to specific events. Additionally, examining emotions in product reviews assists companies in comprehending customer sentiment, allowing them to improve the quality of their products and services. Most research on Indonesian language emotion detection utilizes statistical feature extraction, with limited discussion on the impact of both statistical and semantic feature extraction. Thus, the research aims to detect emotions in short t

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Nastro, F. Fiori, D. Croce, S. Schmidt, R. Basili, and F. Schultze-Lutter. "Insideout project: Using big data and machine learning for prevention in psychiatry." European Psychiatry 64, S1 (2021): S343. http://dx.doi.org/10.1192/j.eurpsy.2021.919.

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IntroductionSocial Media might represent an amazing and valuable source of information on mental health and well-being. Several researches revealed that adolescents aged 13 to 17 years old go “online” daily or stay online “almost constantly”.ObjectivesThe aim of this project is to identify distress in pre-clinical stages using Social media screening methods. The system can be modelled to centre on different several health-related topics.MethodsWe created a digital system able to analyse scripts written by adolescents on Twitter. InsideOut works using machine learning techniques and computation

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AKANDE, PETER ADEWALE, OLUSOLA FESTUS ADELEKE, RASHEED ADIO AJAO, and AKEEM KOLAWOLE AMUSA. "A SEMANTIC ANALYSIS OF SELECTED PETER OBI’S POLITICAL CAMPAIGN TWEETS AND READERS’ COMMENTS." International Journal of Social Sciences and Management Review 06, no. 06 (2023): 197–216. http://dx.doi.org/10.37602/ijssmr.2023.6615.

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The purpose of this study was to analyse the semantic characteristics of Peter Obi's political campaign tweets and comments on Twitter. This study is based on the semantic analysis theoretical framework. This study used a mixed method research design, focusing on Peter Obi's political campaign tweets and readers' comments on Twitter. Data collection involves retrieving tweets and extracting comments via Twitter's API or third-party tools. Data analysis includes rule-based methods, machine learning algorithms, or pre-trained models. A purposive random selection technique is adopted due to impra

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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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Mengelkamp, Aaron, Kevin Koch, and Matthias Schumann. "Creating Sentiment Dictionaries: Process Model and Quantitative Study for Credit Risk." European Conference on Social Media 9, no. 1 (2022): 121–29. http://dx.doi.org/10.34190/ecsm.9.1.167.

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Since textual user generated content from social media platforms contains valuable information for decision support and especially corporate credit risk analysis, automated approaches for text classification such as the application of sentiment dictionaries and machine learning algorithms have received great attention in recent user generated content based research endeavors. While machine learning algorithms require individual training data sets for varying sources, sentiment dictionaries can be applied to texts immediately, whereby domain specific dictionaries attain better results than doma

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Olteanu, Adriana, Alexandra Cernian, and Sebastian-Augustin Gâgă. "Leveraging Machine Learning and Semi-Structured Information to Identify Political Views from Social Media Posts." Applied Sciences 12, no. 24 (2022): 12962. http://dx.doi.org/10.3390/app122412962.

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Social media platforms make a significant contribution to modeling and influencing people’s opinions and decisions, including political views and orientation. Analyzing social media content can reveal trends and key triggers that will influence society. This paper presents an exhaustive analysis of the performance generated by various implementations of the Naïve Bayes classifier, combined with a semi-structured information approach, to identify the political orientation of Twitter users, based on their posts. As research methodology, we aggregate in a semi-structured format a database of over

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Wilson, Theresa, Janyce Wiebe, and Paul Hoffmann. "Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis." Computational Linguistics 35, no. 3 (2009): 399–433. http://dx.doi.org/10.1162/coli.08-012-r1-06-90.

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Many approaches to automatic sentiment analysis begin with a large lexicon of words marked with their prior polarity (also called semantic orientation). However, the contextual polarity of the phrase in which a particular instance of a word appears may be quite different from the word's prior polarity. Positive words are used in phrases expressing negative sentiments, or vice versa. Also, quite often words that are positive or negative out of context are neutral in context, meaning they are not even being used to express a sentiment. The goal of this work is to automatically distinguish betwee

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Alomar, Khalid, and Hamed AL-Rubaiee. "Using Sentiment Analysis of Arabic Tweets to Fine-Tune CRM Structure." Journal of King Abdulaziz University: Computing and Information Technology Sciences 12, no. 1 (2023): 37–50. http://dx.doi.org/10.4197/comp.12-1.4.

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Understanding how customers perceive the services they receive has always been crucial to a business’s success. It is widely accepted that Customer Relationship Management (CRM) and Customer Experience Management (CEM) have both been shown to aid businesses in making better decisions by providing them with better information. Unfortunately, in real-world business applications, there are distinctions between customer opinions collected through customer relationship management (CRM) and the real customer opinions gathered via social media for customer experience management (CEM). It is critical

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Talpur, Bandeh Ali, and Declan O’Sullivan. "Multi-Class Imbalance in Text Classification: A Feature Engineering Approach to Detect Cyberbullying in Twitter." Informatics 7, no. 4 (2020): 52. http://dx.doi.org/10.3390/informatics7040052.

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Twitter enables millions of active users to send and read concise messages on the internet every day. Yet some people use Twitter to propagate violent and threatening messages resulting in cyberbullying. Previous research has focused on whether cyberbullying behavior exists or not in a tweet (binary classification). In this research, we developed a model for detecting the severity of cyberbullying in a tweet. The developed model is a feature-based model that uses features from the content of a tweet, to develop a machine learning classifier for classifying the tweets as non-cyberbullied, and l

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Khatoon, Shaheen, and Lamis Abu Romman. "Domain Independent Automatic Labeling system for Large-scale Social Data using Lexicon and Web-based Augmentation." Information Technology And Control 49, no. 1 (2020): 36–54. http://dx.doi.org/10.5755/j01.itc.49.1.23769.

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Recently, with the large-scale adoption of social media, people have begun to express their opinion on these sites in the form of reviews. Potential consumers often forced to wade through huge amount of reviews to make informed decision. Sentiment analysis has become rapid and effective way to automatically gauge consumers’ opinion. However, such analysis often requires tedious process of manual tagging of large training examples or manually building a lexicon for the purpose of classifying reviews as positive or negative. In this paper, we present a method to automate the tedious process of l

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Madhura, G.K and Puneet Shetteppanavar. "TWITTER SENTIMENT ANALYSIS FOR PRODUCT REVIEWS TO GATHER INFORMATION USING MACHINE LEARNING TECHNIQUE." March 17, 2022. https://doi.org/10.5281/zenodo.6507778.

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The concept of sentiment analysis of twitter data and semantic analysis with the augmentation of machine learning methodologies has become a hot topic in recent years. Many strategies have been presented in the area of sentiment analysis in the last few years to evaluate social media data and produce a graphical presentation towards a certain business. Sentiment analysis shows you how people feel about a product or brand when penning a social media message about it. This is crucial information if you know that one persons opinion of a firm or its products might impact the opinions of others. L

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"Text Preprocessing Method on Twitter Sentiment Analysis using Machine Learning." Regular 9, no. 11 (2020): 233–40. http://dx.doi.org/10.35940/ijitee.k7771.0991120.

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In real world, twitter sentimental analysis (TSA) acting a major role in observing the public opinion about customer side. TSA is complex compared to general sentiment analysis due to pre-processing of text on Twitter. The maximum limit on the number of characters allowed on Twitter is 280. In this article we discuss the influence of the text pre-processing technique on the classification efficiency of emotions in two kinds of classification problems and summarize the classification efficiency of the four pre-processing methods. This paper contributes to the consumer satisfaction classificatio

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Yadav, Jitendra, Madhvendra Misra, Nripendra P. Rana, and Kuldeep Singh. "Exploring the synergy between nano-influencers and sports community: behavior mapping through machine learning." Information Technology & People ahead-of-print, ahead-of-print (2021). http://dx.doi.org/10.1108/itp-03-2021-0219.

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PurposeThe paper aims to explore the influence of cybersecurity on the semantic orientation of the sports consumers. Focusing on both sport and esports, this study finds the social media factors contributing in the sentiment formation and commenting behavior on Twitter and proposes a scheme for attitude modulation through identification of highly engaged nano-influencers.Design/methodology/approachExperimental design was used as the research methodology. Data mining from Twitter using RStudio software was conducted using the keyword “cybersecurity” during the time of pandemic. Final corpus of

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Monika, P., Chaitanya Kulkarni, N. Harish Kumar, S. Shruthi, and V. Vani. "Machine learning approaches for sentiment analysis." International journal of health sciences, April 16, 2022, 1286–300. http://dx.doi.org/10.53730/ijhs.v6ns4.6119.

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Sentiment Analysis or Opinion Mining is popular task of Natural Language Processing (NLP) performed on textual data generated by users to know the orientation or sentiment of the text. To perform Sentiment Analysis, it is critical to create an accurate and precise model, machine learning techniques are heavily utilized to build an accurate model. Deep learning and transfer learning techniques have been found to have increased utilization and better results, making them one of the most popular research areas around the world. Hotel and restaurant industries analyze reviews to obtain a deeper un

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Shoukry, Amira, and Ahmed Rafea. "Machine Learning and Semantic Orientation Ensemble Methods for Egyptian Telecom Tweets Sentiment Analysis." Journal of Web Engineering, June 3, 2020. http://dx.doi.org/10.13052/jwe1540-9589.1924.

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The vast amount of data currently available online attracted many parties to analyze sentiments expressed in these data extracting valuable knowledge. Many approaches have been proposed to classify the posted content utilizing a single classifier. However, it has been proven that ensemble learning and combining multiple classifiers may enhance classification performance. The aim of this study is to improve the Egyptian sentiment classification by combining different classification algorithms. First, we investigated the benefit of combining multiple SO classifiers using different subsets from S

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Chiranjeevi, Phaneendra, and A. Rajaram. "A lightweight deep learning model based recommender system by sentiment analysis." Journal of Intelligent & Fuzzy Systems, April 6, 2023, 1–14. http://dx.doi.org/10.3233/jifs-223871.

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Recommender systems based on sentiment analysis become challenging due to the presence of enormous data available over the internet. With the lack of proper data cleaning and analysis methods, existing machine learning (ML) techniques fail to generate accurate recommendations. To overcome this issue, this paper proposes a Light Deep Learning (LightDL)-based recommender system that uses Twitter-based reviews. First, the data is collected from Twitter and cleaned by subsequent data cleaning processes. Then, this pre-processed data is fed into the LightDL model, which learns the important feature

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Cyril, C. Pretty Diana, J. Rene Beulah, Neelakandan Subramani, Prakash Mohan, A. Harshavardhan, and D. Sivabalaselvamani. "An automated learning model for sentiment analysis and data classification of Twitter data using balanced CA-SVM." Concurrent Engineering, July 20, 2021, 1063293X2110314. http://dx.doi.org/10.1177/1063293x211031485.

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The modern society runs over the social media for their most time of every day. The web users spend their most time in social media and they share many details with their friends. Such information obtained from their chat has been used in several applications. The sentiment analysis is the one which has been applied with Twitter data set toward identifying the emotion of any user and based on those different problems can be solved. Primarily, the data as of the Twitter database is preprocessed. In this step, tokenization, stemming, stop word removal, and number removal are done. The proposed a

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Watanabe, Kohei, and Marius Sältzer. "Semantic temporality analysis: A computational approach to time in English and German texts." Research & Politics 10, no. 3 (2023). http://dx.doi.org/10.1177/20531680231197456.

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Temporality is an important aspect of political discourse. Politicians and policymakers attempt to construct the past and the future to gain power, legitimize their policies, claim success for themselves and blame others. To make computational analysis of temporality more accessible, we develop a new methodology using a semisupervised machine-learning algorithm called Latent Semantic Scaling. Only with a set of common verbs in the past perfect and future tense as seed words, the algorithm estimates the temporality of all other words. We demonstrate that it can identify temporal orientation of

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Cicekyurt, Emre, and Gokhan Bakal. "Enhancing Sentiment Analysis in Stock Market Tweets Through BERT-Based Knowledge Transfer." Computational Economics, February 26, 2025. https://doi.org/10.1007/s10614-025-10901-8.

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Abstract One of the widely studied text classification efforts is sentiment analysis. It is a specific examination involving natural language processing and machine learning methods to understand semantic orientation from textual data. Working social media posts, such as tweets, for sentiment analysis, is quite common among researchers due to the speed of information dissemination. In this regard, forecasting stock market tweets is a widely studied research topic. Some studies have revealed a strong connection between sentiment and stock market performance, while others have not found any nota

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M, Sreenandan Reddy, Anitha Bukkacherla, Chowdeshwari Pujari, Baba Fareed Shaik, Neeraja Narla, and Dileep Kumar Gosala. "NLP-Based Extended Lexicon Model For Sarcasm Detection With Tweets And Emojis." Global Journal of Engineering Innovations and Interdisciplinary Research 5, no. 2 (2025). https://doi.org/10.33425/3066-1226.1088.

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Sarcasm detection in social media, especially on platforms like Twitter, poses a significant challenge due to the informal and context-dependent nature of language. This research presents an NLP-based extended lexicon model for sarcasm detection, leveraging both textual features and emojis to enhance interpretability and accuracy. The proposed model integrates a sentiment lexicon with syntactic and semantic cues, enriched by emoji sentiment analysis, to capture subtle contradictions and ironic tones in tweets. A dataset of labeled tweets is preprocessed and analyzed using natural language proc

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SHUKLA, SHIV SHANKAR PRASAD, and MAHESHWARI PRASAD SINGH. "Stacked Classification Approach using Optimized Hybrid Deep Learning Model for Early Prediction of Behaviour Changes on Social Media." ACM Transactions on Asian and Low-Resource Language Information Processing, August 27, 2024. http://dx.doi.org/10.1145/3689906.

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Detecting signs of suicidal thoughts on social media is paramount for preventing suicides, given the platforms' role as primary outlets for emotional expression. Traditional embedding techniques focus solely on semantic analysis and lack the sentiment analysis essential for capturing emotions. This limitation poses challenges in developing high-accuracy models. Additionally, previous studies often rely on a single dataset, further constraining their effectiveness. To overcome these challenges, this study proposes an innovative approach that integrates embedding techniques such as BERT, which o

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Abdulrahim, Alhaizaey, and Berri Jawad. "Combining Machine Learning and Semantic Analysis for Efficient Misinformation Detection of Arabic Covid-19 Tweets." July 13, 2022. https://doi.org/10.5281/zenodo.6826369.

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With the spread of social media platforms and the proliferation of misleading news, misinformation detection within microblogging platforms has become a real challenge. During the Covid-19 pandemic, many fake news and rumors were broadcasted and shared daily on social media. In order to filter out these fake news, many works have been done on misinformation detection using machine learning and sentiment analysis in the English language. However, misinformation detection research in the Arabic language on social media is limited. This paper introduces a misinformation verification system for Ar

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Aditya Parashar, Nikhil Jadhav, Aniket Madame, and Suhas Deokate. "The Automatization of Social Media Communication." International Journal of Scientific Research in Computer Science, Engineering and Information Technology, May 8, 2023, 183–87. http://dx.doi.org/10.32628/cseit239037.

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This research paper compiles and evaluates several studies that have a particular interest in social media analysis and its applications. Studies on evaluating the impact of movies and television shows on social media platforms, using semantic knowledge graphs to analyze Covid-19 news articles and identify fake news on social media, forecasting social media data using machine learning, and the evolution of the power of central nodes in a Twitter social network are all covered in this article. The significance of sentiment analysis and opinion mining in social media, analysis of social media-ba

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Chaudhary, Meghna, Kristin Kosyluk, Sylvia Thomas, and Tempestt Neal. "On the use of aspect-based sentiment analysis of Twitter data to explore the experiences of African Americans during COVID-19." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-37592-1.

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AbstractAccording to data from the U.S. Center for Disease Control and Prevention, as of June 2020, a significant number of African Americans had been infected with the coronavirus disease, experiencing disproportionately higher death rates compared to other demographic groups. These disparities highlight the urgent need to examine the experiences, behaviors, and opinions of the African American population in relation to the COVID-19 pandemic. By understanding their unique challenges in navigating matters of health and well-being, we can work towards promoting health equity, eliminating dispar

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Yadav, Jitendra, Madhvendra Misra, Nripendra P. Rana, Kuldeep Singh, and Sam Goundar. "Netizens' behavior towards a blockchain-based esports framework: a TPB and machine learning integrated approach." International Journal of Sports Marketing and Sponsorship, December 7, 2021. http://dx.doi.org/10.1108/ijsms-06-2021-0130.

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Purpose Based on the concepts confined in Ajzen's theory of planned behavior (TPB), this study investigates users' attitudes towards adoption of a blockchain-based framework in the esports industry that proposes a scheme of rewarding stakeholders for their invested attention along with blockchain technology's inherent protocols. Design/methodology/approach The present study uses RStudio (Version 1.3.1093) package RedditExtractoR for scraping and analysis of the discussion referring to the keyword “Verasity” on the Reddit website. The final corpus of 1,913 user comments was considered for the s

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Kumar, Sandeep, Sushma Patil, Dewang Subil, Noureen Nasar, Sujatha Arun Kokatnoor, and Balachandran Krishnan. "Text Mining – A Comparative Review of Twitter Sentiments Analysis." Recent Advances in Computer Science and Communications 16 (July 26, 2023). http://dx.doi.org/10.2174/2666255816666230726140726.

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Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate S

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Akintoye, Oluwole Stephen, Ridwan Adebowale Yusuf, Faustus Domebale Maale, and Asenath Aoko Odondi. "Leveraging Natural Language Processing to Detect Suicidal Ideation on Social Media: A Deep Learning Approach." Journal of Frontiers in Multidisciplinary Research, 2022, 440–50. https://doi.org/10.54660/.jfmr.2022.3.1.440-450.

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Suicide remains a global public health crisis, with millions expressing emotional distress and suicidal ideation on social media platforms, often without receiving timely intervention. This study explores the potential of Natural Language Processing (NLP) to detect suicide-related content by leveraging deep learning models trained on real-time user-generated text. Harnessing the power of language, the project aims to develop and benchmark cutting-edge NLP architectures capable of identifying suicidal ideation with high accuracy, precision, and recall. Unlike traditional clinical assessments, s

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Stark, Karen, Amol Shah, JAcob Borgman, et al. "Data Science, Analytics and Collaboration for a Biosurveillance Ecosystem." Online Journal of Public Health Informatics 11, no. 1 (2019). http://dx.doi.org/10.5210/ojphi.v11i1.9702.

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ObjectiveWhile there is a growing torrent of data that disease surveillance could leverage, few effective tools exist to help public health professionals make sense of this data or that provide secure work-sharing and communication. Meanwhile, our ever more-connected world provides an increasingly receptive environment for diseases to emerge and spread rapidly making early warning and collaborative decision-making essential to saving lives and reducing the impact of outbreaks. Digital Infuzion's previous work on the Defense Threat Reduction Agency (DTRA)'s Biosurveillance Ecosystem (BSVE) buil

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