Abstract
Sentiment Analysis of tweets is a complex task, because these short messages employ unconventional language to increase the expressiveness. This task becomes even more difficult when people use figurative language (e.g. irony, sarcasm and metaphors) because it causes a mismatch between the literal meaning and the actual expressed sentiment. In this paper, we describe a sentiment analysis system designed for handling ironic and sarcastic tweets. Features grounded on several linguistic levels are proposed and used to classify the tweets in a 11-scale range, using a decision tree. The system is evaluated on the dataset released by the organizers of the SemEval 2015, task 11. The results show that our method largely outperforms the systems proposed by the participants of the task on ironic and sarcastic tweets.
Original language | English |
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Title of host publication | 29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015 |
Publisher | Shanghai Jiao Tong University |
Pages | 178-187 |
Number of pages | 10 |
Publication status | Published - 1 Jan 2015 |
Event | 29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015 - Shanghai, China Duration: 30 Oct 2015 → 1 Nov 2015 |
Conference
Conference | 29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015 |
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Country/Territory | China |
City | Shanghai |
Period | 30/10/15 → 1/11/15 |
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Linguistics and Language