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        <title>Challenges of Evaluating Sentiment Analysis Tools on Social Media</title>
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    <abstract>This paper discusses the challenges in carrying out fair comparative evaluations of sentiment analysis systems. Firstly, these are due to differences in corpus annotation guidelines and sentiment class distribution. Secondly, different systems often make different assumptions about how to interpret certain statements, e.g. tweets with URLs. In order to study the impact of these on evaluation results, this paper focuses on tweet sentiment analysis in particular. One existing and two newly created corpora are used, and the performance of four different sentiment analysis systems is reported; we make our annotated datasets and sentiment analysis applications publicly available. We see considerable variations in results across the different corpora, which calls into question the validity of many existing annotated datasets and evaluations, and we make some observations about both the systems and the datasets as a result.</abstract>
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%0 Conference Proceedings
%T Challenges of Evaluating Sentiment Analysis Tools on Social Media
%A Maynard, Diana
%A Bontcheva, Kalina
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F maynard-bontcheva-2016-challenges
%X This paper discusses the challenges in carrying out fair comparative evaluations of sentiment analysis systems. Firstly, these are due to differences in corpus annotation guidelines and sentiment class distribution. Secondly, different systems often make different assumptions about how to interpret certain statements, e.g. tweets with URLs. In order to study the impact of these on evaluation results, this paper focuses on tweet sentiment analysis in particular. One existing and two newly created corpora are used, and the performance of four different sentiment analysis systems is reported; we make our annotated datasets and sentiment analysis applications publicly available. We see considerable variations in results across the different corpora, which calls into question the validity of many existing annotated datasets and evaluations, and we make some observations about both the systems and the datasets as a result.
%U https://aclanthology.org/L16-1182
%P 1142-1148

Markdown (Informal)

[Challenges of Evaluating Sentiment Analysis Tools on Social Media](https://aclanthology.org/L16-1182) (Maynard & Bontcheva, LREC 2016)

• Challenges of Evaluating Sentiment Analysis Tools on Social Media (Maynard & Bontcheva, LREC 2016)

ACL

• Diana Maynard and Kalina Bontcheva. 2016. Challenges of Evaluating Sentiment Analysis Tools on Social Media. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1142–1148, Portorož, Slovenia. European Language Resources Association (ELRA).