The State-of-the-Art in Twitter Sentiment Analysis: A Review and Benchmark Evaluation

Authors:
David Zimbra

Santa Clara University, Santa Clara, CA, USA

Santa Clara University, Santa Clara, CA, USA
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,
Ahmed Abbasi

University of Virginia, Charlottesville, VA, USA

University of Virginia, Charlottesville, VA, USA
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,
Daniel Zeng

University of Arizona, Tucson, AZ, USA

University of Arizona, Tucson, AZ, USA
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,
Hsinchun Chen

University of Arizona, Tucson, AZ, USA

University of Arizona, Tucson, AZ, USA
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Authors Info & Claims

ACM Transactions on Management Information Systems Volume 9 Issue 2Article No.: 5pp 1–29https://doi.org/10.1145/3185045

Published:24 August 2018Publication History

ACM Transactions on Management Information Systems

Abstract

Twitter has emerged as a major social media platform and generated great interest from sentiment analysis researchers. Despite this attention, state-of-the-art Twitter sentiment analysis approaches perform relatively poorly with reported classification accuracies often below 70%, adversely impacting applications of the derived sentiment information. In this research, we investigate the unique challenges presented by Twitter sentiment analysis and review the literature to determine how the devised approaches have addressed these challenges. To assess the state-of-the-art in Twitter sentiment analysis, we conduct a benchmark evaluation of 28 top academic and commercial systems in tweet sentiment classification across five distinctive data sets. We perform an error analysis to uncover the causes of commonly occurring classification errors. To further the evaluation, we apply select systems in an event detection case study. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation and provide suggestions to guide the design of the next generation of approaches.

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Index Terms

The State-of-the-Art in Twitter Sentiment Analysis: A Review and Benchmark Evaluation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information systems applications
    1. Data mining

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Published in
ACM Transactions on Management Information Systems Volume 9, Issue 2
Regular Papers and Research Commentary
June 2018
113 pages
ISSN:2158-656X
EISSN:2158-6578
DOI:10.1145/3210372
Editor:
Daniel Zeng
University of Arizona, USA
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Publication History
- Published: 24 August 2018
- Accepted: 1 January 2018
- Revised: 1 September 2017
- Received: 1 August 2016
Published in tmis Volume 9, Issue 2

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Sentiment analysis
benchmark evaluation
natural language processing
opinion mining
social media
text mining
twitter
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The State-of-the-Art in Twitter Sentiment Analysis: A Review and Benchmark Evaluation

ACM Transactions on Management Information Systems

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