Twitter Sentiment Classification Using Binary Shuffled Frog Algorithm. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Yuvaraj, N.; Sabari, A.
• Is Part Of: Intelligent automation & soft computing. Volume 23:Issue 2(2017)
• Page Start: 373
• Page End: 381
• Publication Date: 2017-04-03
• Subjects: Twitter -- sentiments -- twitter sentiment classification algorithm -- feature selection and Shuffled Frog Algorithm; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 003.5
• DOI: 10.1080/10798587.2016.1231479
• Languages: English
• ISSNs: 1079-8587

Abstract

Abstract: Twitter is a popular social networking site allowing users to read/post messages (tweets). Among the topic varieties, people in Twitter express sentiments for brands, stars, merchandises, and social events. Hence, it draws attention to assess a crowd's sentiments in Twitter. Tweets classify a target's sentiments as positive, negative or neutral. Individuals comment on many entities (or targets) in a tweet, thereby affecting availabilities for current methods. This is beneficial for clients who explore products sentiment before acquisition, or corporations wanting to check public sentiment of their products. This work proposes a new Twitter Sentiment Classification algorithm using novel feature selection technique with ensemble classifier through a meta-heuristic algorithm. Feature vectors are represented using binary encoding and a novel transfer function to flip encoding bits using shuffled frog meta-heuristic algorithm is proposed. To evaluate the new algorithm, Twitter corpus from Stanford University is used.