International Journal of Advanced Research in Computer and Communication Engineering

A monthly peer-reviewed online and print journal

ISSN Online 2278-1021
ISSN Print 2319-5940

Since 2012

Abstract: This paper is a report of a review on Sentiment Analysis for classification of Opinions on social Networking sites that explored the methods, social media platform used and its application. . In this a generic deep learning framework for predictive analytics utilizing both structured and unstructured data will be presented. The social media, blogs, forums, e-commerce web sites, and so on encourages people to share their opinion, emotions and feelings publicly. Today these Internet Sites are very popular and this resulted a huge amount of raw data that has been uploaded by users in the form of text, photos, audio and videos. People’s sentiments and experiences are very valuable information in decision making process of any business but to get benefit from these opinion and experiences, the accumulated content should be extracted and analysed properly. This can be done by using sentiment analysis. A systematic review of studies published between 2015-2020 was undertaken using the following trusted and credible database including IEEE Xplore, ACM, Emerald Insight, Science Direct and Scopus. After the initial and in-depth screening of paper, 24 out of 85 articles have been chosen from the review process. The articles have been reviewed based on the aim and objectives of the study. The result shows most of the articles applied opinion-lexicon method to analyses text sentiment in social media, extracted data on microblogging site mainly Twitter and sentiment analysis application can be seen in world events, as improving quality and strategy in business, Market Research, Decision Making, political forecasting an election result, monitor disease outbreak, create awareness on the importance of data security, perception towards a particular spot, and improve locate and response to the disaster.

Keywords: Opinion, Sentiment, Machine learning (ML), Natural Language Processing (NLP), Social media.


PDF | DOI: 10.17148/IJARCCE.2021.105100

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