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Twitter Sentiment Classification Using Binary Shuffled Frog Algorithm


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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 crowd2019s sentiments in Twitter. Tweets classify a target2019s 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.


Keywords


Pages

Total Pages: 9
Pages: 373-381

DOI
10.1080/10798587.2016.1231479


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Published

Volume: 23
Issue: 2
Year: 2016

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References

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JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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