Manuscript Number : CSEIT1833227
A Sentiment Computing for the Opinions Based on the Twitter
Authors(4) :-Pooja Dhamanekar, Pooja Bindage, Chetan Arage, Mahesh Gaikwad The era of social networking has increased the amount of data generated by the user. People from all over the world share their opinions and thoughts on the micro-blogging sites on daily basis. As the use of internet such as websites, social networks, and blogs increases online portals reviews, opinions, recommendations, ratings, and feedbacks are also generated by users. Twitter is one of the most widely used micro-blogging site where people share their reviews in the form of tweets. This user can give their opinion on anything like books, people, hotels, products, research, events, etc. These sentiments become very useful for businesses, governments, and individuals. However, there are several challenges facing the sentiment analysis and evaluation process. These challenges become mountain in analyzing the accurate meaning of sentiments and measuring sentiment polarity. Therefore, we propose an innovative method to do the sentiment computing for opinions. Our method is based on the social media data of a Tweets, a Word Emotion Association Network (WEAN) is built to jointly express its semantics and emotions, which lays the foundation for the opinion sentiment computation.
Pooja Dhamanekar Sentiment computing, Emotion classiï¬cation, Social media big data, Opinions, Text mining. Publication Details Published in : Volume 3 | Issue 3 | March-April 2018 Article Preview
Professor, Department of Computer Science and Engineering, Sanjay Ghodawat Institute Atigre, Kolhapur, Maharashtra, India
Pooja Bindage
Professor, Department of Computer Science and Engineering, Sanjay Ghodawat Institute Atigre, Kolhapur, Maharashtra, India
Chetan Arage
Mahesh Gaikwad
Date of Publication : 2018-04-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 899-904
Manuscript Number : CSEIT1833227
Publisher : Technoscience Academy