Publication Type : Conference Paper
Publisher : 2020 International Conference on Inventive Computation Technologies (ICICT)
Source : 2020 International Conference on Inventive Computation Technologies (ICICT) (2020)
Url : https://ieeexplore.ieee.org/abstract/document/9112572
Keywords : behavioural sciences computing, Data mining, data science community, digital well-being, emotional quotient, Information Retrieval, learning (artificial intelligence), Machine learning algorithms, microblogging websites, opinion mining, opinion mining procedures, Pattern classification, search engine, Search engines, Sentiment analysis, sentiment classifiers, social networking, text analysis, Twitter, Web sites
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2020
Abstract : Opinion mining on various issues is a very popular trend in the micro-blogging research community. Advanced data mining techniques using sentiment analysis and machine learning algorithms on large datasets like microblogging websites are popular and trending in the data science community. But such analytics are limited to only certain aspects of interest. In this paper, we present a search engine that presents a user's emotions over a timeline. This would serve as a novel approach to adding search capability over opinion mining procedures and exploring potential optimizations for a wide range of features and methods for training sentiment classifiers. Users should be able to chart down their emotional quotient using the proposed application. This would aid in promoting their digital well-being.
Cite this Research Publication : Ritwik Murali, Ravi, A., and Agarwal, H., “Enhancing Digital Well-being using Opinion Mining and Sentiment Classifiers”, in 2020 International Conference on Inventive Computation Technologies (ICICT), 2020.