Abstract
Twitter has become one of the quintessential social media platforms for user-generated content. Researchers and industry practitioners are increasingly interested in Twitter sentiments. Consequently, an array of commercial and freely available Twitter sentiment analysis tools have emerged, though it remains unclear how well these tools really work. This study presents the findings of a detailed benchmark analysis of Twitter sentiment analysis tools, incorporating 20 tools applied to 5 different test beds. In addition to presenting detailed performance evaluation results, a thorough error analysis is used to highlight the most prevalent challenges facing Twitter sentiment analysis tools. The results have important implications for various stakeholder groups, including social media analytics researchers, NLP developers, and industry managers and practitioners using social media sentiments as input for decision-making.- Anthology ID:
- L14-1406
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 823–829
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/483_Paper.pdf
- DOI:
- Cite (ACL):
- Ahmed Abbasi, Ammar Hassan, and Milan Dhar. 2014. Benchmarking Twitter Sentiment Analysis Tools. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 823–829, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- Benchmarking Twitter Sentiment Analysis Tools (Abbasi et al., LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/483_Paper.pdf