SemEval-2016 task 5 : aspect based sentiment analysis
Maria Pontiki,Dimitris Galanis,Haris Papageorgiou,Ion Androutsopoulos,Suresh Manandhar,Mohammad AL-Smadi,Mahmoud Al-Ayyoub,Yanyan Zhao,Bing Qin,Orphée De Clercq,Veronique Hoste,Marianna Apidianaki,Xavier Tannier,Natalia V. Loukachevitch,Evgeniy V. Kotelnikov,Núria Bel,Salud María Jiménez-Zafra,Gülşen Eryiğit +17 more
- pp 19-30
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TLDR
This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015, which attracted 245 submissions from 29 teams and provided 19 training and 20 testing datasets.Abstract:
This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams.read more
Citations
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Proceedings ArticleDOI
SemEval-2017 Task 4: Sentiment Analysis in Twitter
TL;DR: Crowdourcing on Amazon Mechanical Turk was used to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks, which included two subtasks: A, an expression-level subtask, and B, a message level subtask.
Proceedings ArticleDOI
SemEval-2016 Task 4: Sentiment Analysis in Twitter
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Deep Learning--based Text Classification: A Comprehensive Review
TL;DR: This paper provided a comprehensive review of more than 150 deep learning-based models for text classification developed in recent years, and discussed their technical contributions, similarities, and strengths, and provided a quantitative analysis of the performance of different deep learning models on popular benchmarks.
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