Open AccessMulti-class Emotion AI by reconstructing linguistic context of words
Author(s) -
Kallol Roy,
Farhaan Ahmed Shaik,
K. V. D. Kiran,
Mirfakhraiee Teja,
Subhani Kurra
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.20.11763
Subject(s) - sentiment analysis , word2vec , slang , social media , microblogging , computer science , class (philosophy) , product (mathematics) , context (archaeology) , point (geometry) , emotion classification , artificial intelligence , natural language processing , world wide web , linguistics , paleontology , philosophy , geometry , mathematics , embedding , biology
In today’s technological world, Social networking websites like Twitter, Instagram, Facebook, Tumblr, etc. play a very significant role. Emotion AI is about dealing, recognizing and analyzing sentiments or opinions conveyed in a person’s text. In particular Emotion is most frequently called Sentiment analysis. It helps us to understand the people’s point of view. A vast amount of sentiment rich data is produced by Social networking websites in the form of posts, tweets, statuses, blogs etc. Some users post reviews of certain products in social media which influences customers to buy the product. Companies can use such review data analyze it and improve the product. Sentiment analysis of Twitter is troublesome correlated to other social networking websites because of the existence of a lot of short words, misspellings and slang words applying emotion analysis to such data is more challenging. We have classified the sentiment into 5 categories. Machine learning strategies are preferred mostly for analyzing emotion AI. We have used neural network model word2vec with TF-IDF approach to predict the sentiment of the tweet.