@article{321eb1b428274311b509c517e6e2ff9a,
title = "The state-of-the-art in twitter sentiment analysis: A review and benchmark evaluation",
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.",
keywords = "Benchmark evaluation, Natural language processing, Opinion mining, Sentiment analysis, Social media, Text mining, Twitter",
author = "David Zimbra and Ahmed Abbasi and Daniel Zeng and Hsinchun Chen",
note = "Funding Information: This work is supported by the National Science Foundation under grants IIS-1553109, IIS-1236970, BDS-1636933, CCF-1629450, and ACI-1443019, the MOST Grant 2016QY02D0305, the NNSFC Innovative Team Grant 71621002, the CAS Grant ZDRW-XH-2017-3, and the NIH Grant 5R01DA037378-04. Authors{\textquoteright} addresses: D. Zimbra, Operations Management & Information Systems Department, Santa Clara University, Santa Clara, CA, USA; email: dzimbra@scu.edu; A. Abbasi, Information Technology Area and Center for Business Analytics, University of Virginia, Charlottesville, VA, USA; email: abbasi@comm.virginia.edu; D. Zeng, Management Information Systems Department, University of Arizona, Tucson, AZ, USA; email: zeng@eller.arizona.edu; H. Chen, Artificial Intelligence Lab, University of Arizona, Tucson, AZ, USA; email: hchen@eller.arizona.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. {\textcopyright} 2018 ACM 2158-656X/2018/08-ART5 $15.00 https://doi.org/10.1145/3185045 Publisher Copyright: {\textcopyright} 2018 ACM 2158-656X/2018/08-ART5 $15.00",
year = "2018",
month = apr,
doi = "10.1145/3185045",
language = "English (US)",
volume = "9",
journal = "ACM Transactions on Management Information Systems",
issn = "2158-656X",
publisher = "Association for Computing Machinery (ACM)",
number = "2",
}