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An Overview of Methodologies and Challenges in Sentiment Analysis on Social Networks

An Overview of Methodologies and Challenges in Sentiment Analysis on Social Networks

Aditya Suresh Salunkhe, Pallavi Vijay Chavan
Copyright: © 2020 |Pages: 10
ISBN13: 9781799801061|ISBN10: 1799801063|EISBN13: 9781799801078
DOI: 10.4018/978-1-7998-0106-1.ch010
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MLA

Salunkhe, Aditya Suresh, and Pallavi Vijay Chavan. "An Overview of Methodologies and Challenges in Sentiment Analysis on Social Networks." Handbook of Research on Big Data Clustering and Machine Learning, edited by Fausto Pedro Garcia Marquez, IGI Global, 2020, pp. 204-213. https://doi.org/10.4018/978-1-7998-0106-1.ch010

APA

Salunkhe, A. S. & Chavan, P. V. (2020). An Overview of Methodologies and Challenges in Sentiment Analysis on Social Networks. In F. Garcia Marquez (Ed.), Handbook of Research on Big Data Clustering and Machine Learning (pp. 204-213). IGI Global. https://doi.org/10.4018/978-1-7998-0106-1.ch010

Chicago

Salunkhe, Aditya Suresh, and Pallavi Vijay Chavan. "An Overview of Methodologies and Challenges in Sentiment Analysis on Social Networks." In Handbook of Research on Big Data Clustering and Machine Learning, edited by Fausto Pedro Garcia Marquez, 204-213. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0106-1.ch010

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Abstract

The expeditious increase in the adoption of social media over the last decade, determining and analyzing the attitude and opinion of masses related to a particular entity, has gained quite an importance. With the landing of the Web 2.0, many internet products like blogs, community chatrooms, forums, microblog are serving as a platform for people to express themselves. Such opinion is found in the form of messages, user-comments, news articles, personal blogs, tweets, surveys, status updates, etc. With sentiment analysis, it is possible to eliminate the need to manually going through each and every user comment by focusing on the contextual polarity of the text. Analyzing the sentiments could serve a number of applications like advertisements, recommendations, quality analysis, monetization provided on the web services, real-time analysis of data, analyzing notions related to candidates during election campaign, etc.

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