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.
| Original language | English (US) |
|---|---|
| Article number | 5 |
| Journal | ACM Transactions on Management Information Systems |
| Volume | 9 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 1 2018 |
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Keywords
- Benchmark evaluation
- Natural language processing
- Opinion mining
- Sentiment analysis
- Social media
- Text mining
ASJC Scopus subject areas
- Management Information Systems
- Computer Science(all)
Cite this
The state-of-the-art in twitter sentiment analysis : A review and benchmark evaluation. / Zimbra, David; Abbasi, Ahmed; Zeng, Dajun; Chen, Hsinchun.
In: ACM Transactions on Management Information Systems, Vol. 9, No. 2, 5, 01.04.2018.Research output: Contribution to journal › Review article
}
TY - JOUR
T1 - The state-of-the-art in twitter sentiment analysis
T2 - A review and benchmark evaluation
AU - Zimbra, David
AU - Abbasi, Ahmed
AU - Zeng, Dajun
AU - Chen, Hsinchun
PY - 2018/4/1
Y1 - 2018/4/1
N2 - 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.
AB - 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.
KW - Benchmark evaluation
KW - Natural language processing
KW - Opinion mining
KW - Sentiment analysis
KW - Social media
KW - Text mining
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85053528947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053528947&partnerID=8YFLogxK
U2 - 10.1145/3185045
DO - 10.1145/3185045
M3 - Review article
AN - SCOPUS:85053528947
VL - 9
JO - ACM Transactions on Management Information Systems
JF - ACM Transactions on Management Information Systems
SN - 2158-656X
IS - 2
M1 - 5
ER -