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 Conference Paper Faculty Publications- Conference Papers 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 Chinmayi, P.; Jeswani, Himanshi; Singh, Nandini; Vinay, M.; Jayapriya, J.; Jayadurga, R. Title A name given to the resource Unveiling Sentiment Trends: An Approach to Utilize Machine Learning in Studying User Activities on New Social Applications 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 Lecture Notes in Networks and Systems;Volume;1377 LNNS;pp.259-269 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-5370-6_19" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5370-6_19</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018804923?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018804923?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 Chinmayi P., Christ University, Karnataka, Bangalore, India; Jeswani H., Christ University, Karnataka, Bangalore, India; Singh N., Christ University, Karnataka, Bangalore, India; Vinay M., Christ University, Karnataka, Bangalore, India; Jayapriya J., Christ University, Karnataka, Bangalore, India; Jayadurga R., Christ University, Karnataka, Bangalore, India Description An account of the resource Sentiment analysis is the examination of textual data to determine the writer's attitude, which can be positive, negative, or neutral. In the context of social media analysis, sentiment analysis is peculiar as it helps to identify trends in large amounts of data that are posted by social media users. In the case of sentiment analysis algorithms, the text is categorized into positive, negative, and neutral. Classification of sentiments involves the use of several algorithms such as the decision tree, support vectors, and neural networks. In other words, the paper intends to determine the users sentiment using the decision tree model. Some of the common data sets that have been utilized in this study include the COVID-19 pandemic data, movie reviews, and product ratings. What is tried to be accomplished in this type of case is to determine the efficiency and stability of the decision trees, as well as their optimum success region. Based on the results, it can be pointed out that the accuracy is the highest for the COVID-19 Tweets dataset when referring to the simulation model, which is 98%; hence, the decision tree is best used in the context of the health sector. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Accuracy; Decision trees; Machine learning techniques; Sentimental analysis Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981965369-0; Language A language of the resource English Type The nature or genre of the resource Conference paper 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