Relevant bibliographies by topics / Machine Learning Semantic Orientation Sentiment Analysis Twitter

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Academic literature on the topic 'Machine Learning Semantic Orientation Sentiment Analysis Twitter'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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

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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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Dissertations / Theses on the topic "Machine Learning Semantic Orientation Sentiment Analysis Twitter"

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Fernando, Henriques. "Estudo sobre análise de sentimentos em textos." Master's thesis, Universidade de Évora, 2013. http://hdl.handle.net/10174/18267.

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O sentimento em opiniões terceiras sempre foi e continua a despertar interesse e extrema preocupação por parte dos gestores ou tomadores de decisões. Terceiros podem ser indivíduos, produtos, entidades empresariais, instituições de pesquisa e órgãos governamentais etc. dado que força de expressões e ideias controversas podem causar grandes celeumas. Desta forma o feedback emocional pode ser propulsor de mudanças, no sentido de proporcionar a busca contínua de melhorias por um lado ou determinar por outro o insucesso da entidade. Logo a análise de sentimentos é uma ferramenta indispensável no a
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Book chapters on the topic "Machine Learning Semantic Orientation Sentiment Analysis Twitter"

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Gupta, Neha, and Rashmi Agrawal. "Impact of Deep Learning on Semantic Sentiment Analysis." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch083.

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Online social media (forums, blogs, and social networks) are increasing explosively, and utilization of these new sources of information has become important. Semantics plays a significant role in accurate analysis of an emotion speech context. Adding to this area, the already advanced semantic technologies have proven to increase the precision of the tests. Deep learning has emerged as a prominent machine learning technique that learns multiple layers or data characteristics and delivers state-of-the-art output. Throughout recent years, deep learning has been widely used in the study of sentiments, along with the growth of deep learning in many other fields of use. This chapter will offer a description of deep learning and its application in the analysis of sentiments. This chapter will focus on the semantic orientation-based approaches for sentiment analysis. In this work, a semantically enhanced methodology for the annotation of sentiment polarity in Twitter/ Facebook data will be presented.
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Gupta, Neha, and Rashmi Agrawal. "Impact of Deep Learning on Semantic Sentiment Analysis." In Examining the Impact of Deep Learning and IoT on Multi-Industry Applications. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7511-6.ch007.

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Online social media (forums, blogs, and social networks) are increasing explosively, and utilization of these new sources of information has become important. Semantics plays a significant role in accurate analysis of an emotion speech context. Adding to this area, the already advanced semantic technologies have proven to increase the precision of the tests. Deep learning has emerged as a prominent machine learning technique that learns multiple layers or data characteristics and delivers state-of-the-art output. Throughout recent years, deep learning has been widely used in the study of sentiments, along with the growth of deep learning in many other fields of use. This chapter will offer a description of deep learning and its application in the analysis of sentiments. This chapter will focus on the semantic orientation-based approaches for sentiment analysis. In this work, a semantically enhanced methodology for the annotation of sentiment polarity in Twitter/ Facebook data will be presented.
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Agarwal, Basant, and Namita Mittal. "Machine Learning Approaches for Sentiment Analysis." In Big Data. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9840-6.ch088.

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Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.
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Agarwal, Basant, and Namita Mittal. "Machine Learning Approaches for Sentiment Analysis." In Data Mining and Analysis in the Engineering Field. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6086-1.ch011.

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Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.
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Gupta, Neha, and Rashmi Agrawal. "Integrating Semantic Acquaintance for Sentiment Analysis." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch007.

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The use of emerging digital information has become significant and exponential, as well as the boom of social media (forms, blogs, and social networks). Sentiment analysis concerns the statistical analysis of the views expressed in written texts. In appropriate evaluations of the emotional context, semantics plays an important role. The analysis is generally done from two viewpoints: how semantics are coded in sentimental instruments, such as lexicon, corporate, and ontological, and how automated systems determine feelings on social data. Two approaches to evaluate sentiments are commonly adopted (i.e., approaches focused on machine learning algorithms and semantic approaches). The precise testing in this area was increased by the already advanced semantic technology. This chapter focuses on semantic guidance-based sentiment analysis approaches. The Twitter/Facebook data will provide a semantically enhanced technique for annotation of sentiment polarity.
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Gupta, Neha, and Rashmi Agrawal. "Integrating Semantic Acquaintance for Sentiment Analysis." In Advanced Concepts, Methods, and Applications in Semantic Computing. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6697-8.ch005.

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The use of emerging digital information has become significant and exponential, as well as the boom of social media (forms, blogs, and social networks). Sentiment analysis concerns the statistical analysis of the views expressed in written texts. In appropriate evaluations of the emotional context, semantics plays an important role. The analysis is generally done from two viewpoints: how semantics are coded in sentimental instruments, such as lexicon, corporate, and ontological, and how automated systems determine feelings on social data. Two approaches to evaluate sentiments are commonly adopted (i.e., approaches focused on machine learning algorithms and semantic approaches). The precise testing in this area was increased by the already advanced semantic technology. This chapter focuses on semantic guidance-based sentiment analysis approaches. The Twitter/Facebook data will provide a semantically enhanced technique for annotation of sentiment polarity.
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R., Anto Arockia Rosaline, and Parvathi R. "Deep Learning for Social Media Text Analytics." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch043.

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Text analytics is the process of extracting high quality information from the text. A set of statistical, linguistic, and machine learning techniques are used to represent the information content from various textual sources such as data analysis, research, or investigation. Text is the common way of communication in social media. The understanding of text includes a variety of tasks including text classification, slang, and other languages. Traditional Natural Language Processing (NLP) techniques require extensive pre-processing techniques to handle the text. When a word “Amazon” occurs in the social media text, there should be a meaningful approach to find out whether it is referring to forest or Kindle. Most of the time, the NLP techniques fail in handling the slang and spellings correctly. Messages in Twitter are so short such that it is difficult to build semantic connections between them. Some messages such as “Gud nite” actually do not contain any real words but are still used for communication.
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Asha, K., and K. A. Venkatesh. "A Systematic Review With Recommendations on Intelligent Systems in Cognitive Healthcare." In Intelligent Solutions for Cognitive Disorders. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1090-8.ch001.

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Today's doctors and patients rely on online platforms to express opinions on health matters. Public health importance, especially after the COVID-19 pandemic in both developing and developed countries are focusing on building sustainable, resilient health systems and implementing policies to address prevailing healthcare challenges. Adoption of latest trends and technologies leads to the processing of extensive bigdata by heavy statistical evaluations through machine learning and deep learning techniques to predict prominent semantic patterns. The objective of this chapter is to spotlight on cognitive analysis to extract opinions expressed by users. Authors emphasize narrowing down the new potentials in the field of sentiment analysis through cognition and understanding variations in semantic orientation. The chapter explores new methods and procedures that benefit practitioners and researchers with respect to sentiment polarity using cognitive analytics to address the improvements in healthcare industry.
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Conference papers on the topic "Machine Learning Semantic Orientation Sentiment Analysis Twitter"

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Gautam, Geetika, and Divakar Yadav. "Sentiment analysis of twitter data using machine learning approaches and semantic analysis." In 2014 Seventh International Conference on Contemporary Computing (IC3). IEEE, 2014. http://dx.doi.org/10.1109/ic3.2014.6897213.

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Kanavos, Andreas, Nikos Antonopoulos, Alaa Mohasseb, and Phivos Mylonas. "Analyzing Public Sentiment Towards the Covid-19 Pandemic: A Twitter-Based Sentiment Analysis and Machine Learning Approach." In 2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP). IEEE, 2023. http://dx.doi.org/10.1109/smap59435.2023.10255176.

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Waila, P., Marisha, V. K. Singh, and M. K. Singh. "Evaluating Machine Learning and Unsupervised Semantic Orientation approaches for sentiment analysis of textual reviews." In 2012 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2012. http://dx.doi.org/10.1109/iccic.2012.6510235.

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Manias, George, Maria Angeles Sanguino, Sergio Salmeron, Argyro Mavrogiorgou, Athanasios Kiourtis, and Dimosthenis Kyriazis. "Utilizing an Entity-level Semantic Analysis Approach Towards Enhanced Policy Making." In 4th International Conference on Natural Language Processing and Machine Learning. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130801.

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The tremendous growth and usage of social media in modern societies have led to the production of an enormous real-time volume of social texts and posts, including tweets, that are being produced by users. These collections of social data can be potentially useful, but the extent of meaningful data in these collections is still of high research and business interest. One of the main elements in several application domains, such as policy making, addresses the scope of public opinion analysis. The latter is recently realized through sentiment analysis and Natural Language Processing (NLP), for
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Abidi, K., and K. Smaili. "Creating Multi-Scripts Sentiment Analysis Lexicons for Algerian, Moroccan and Tunisian Dialects." In 2nd International Conference on Machine Learning Techniques and NLP (MLNLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111413.

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In this article, we tackle the issue of sentiment analysis in three Maghrebi dialects used in social networks. More precisely, we are interested by analysing sentiments in Algerian, Moroccan and Tunisian corpora. To do this, we built automatically three lexicons of sentiments, one for each dialect. Each lexicon is composed of words with their polarities, a dialect word could be written in Arabic or in Latin scripts. These lexicons may include French or English words as well as words in Arabic dialect and standard Arabic. The semantic orientation of a word represented by an embedding vector is
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Mileski, Matheus, Daniel Prado Campos, Luiz Fernando Carvalho, and Rafael Gomes Mantovani. "Sentiment Analysis in Social Networks during the 2021 and 2022 Formula 1 Seasons: A Study Using Natural Language Processing on Twitter." In Computer on the Beach. Universidade do Vale do Itajaí, 2024. http://dx.doi.org/10.14210/cotb.v15.p236-243.

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ABSTRACTSentiment Analysis is an emerging research area that focuses onextracting semantic and emotional inferences from natural language,paving the way for analyses that deal with a high volume oftextual data. The growing importance of data in strategic decisionmakingand the recognition of social networks as vast repositoriesof public opinion have propelled this study, which aimed to explorethe interaction between human emotions and motorsport events.Thus, this study focused on applying Natural Language Processingto extract and analyze sentiments expressed in tweets about Formula1. Advanced m
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