Sentiment analyzer with rich features for ironic and sarcastic tweets

Piyoros Tungthamthiti, Enrico Santus, Hongzhi Xu, Chu-ren Huang, Kiyoaki Shirai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

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 languageEnglish
Title of host publication29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015
PublisherShanghai Jiao Tong University
Pages178-187
Number of pages10
Publication statusPublished - 1 Jan 2015
Event29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015 - Shanghai, China
Duration: 30 Oct 20151 Nov 2015

Conference

Conference29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015
CountryChina
CityShanghai
Period30/10/151/11/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

Cite this