Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Reddy, Bhavanam Amarnath; Nayak, Ullal Akshatha; Srinivasan, S.; Angela Jean Mary, E.; Chauhan, Amit; Rajagopal, R. Title A name given to the resource Sentiment and emotion analysis using machine learning techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Exploring Psychology, Social Innovation and Advanced Applications of Machine Learning;pp.173-190 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-6910-4.ch009" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-6910-4.ch009</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000782697?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000782697?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Reddy B.A., Vignana Jyothi Institute of Management, India; Nayak U.A., Nitte Meenakshi Institute of Technology, India; Srinivasan S., AMET University, India; Angela Jean Mary E., St. Claret College, India; Chauhan A., Christ University, India; Rajagopal R., Alliance University, India Description An account of the resource Sentiment analysis and text emotion identification have grown in prominence due to their wide range of applications in fields such as psychology, artificial intelligence, human-computer interaction, and so on. There are numerous Machine Learning approaches available for emotion recognition and sentiment analysis. The chapter also delves into the key procedures of data collecting, preprocessing, and emphasising the necessity of good data in training effective models. Real-world applications from a variety of disciplines, including business, healthcare, and entertainment, are investigated to demonstrate the practical utility of these strategies. The chapter also covers the issues of ambiguity, context-awareness, and cross-linguistic disparities, as well as providing suggestions for future study paths. This chapter provides a detailed exploration of machine learning approaches to sentiment and emotion analysis, making it a valuable resource for researchers, practitioners, and students interested in using machine learning to understand and interpret emotional content in textual data. 2025, IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836936912-8; 979-836936910-4; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online