Tweester at SemEval-2016 task 4: Sentiment analysis in twitter using semantic-affective model adaptation

Elisavet Palogiannidi, Athanasia Kolovou, Fenia Christopoulou, Filippos Kokkinos, Elias Iosif, Nikolaos Malandrakis, Harris Papageorgiou, Shrikanth Narayanan, Alexandros Potamianos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the subtask B. Our system comprises of multiple independent models such as neural networks, semantic-affective models and topic modeling that are combined in a probabilistic way. The novelty of the system is the employment of a topic modeling approach in order to adapt the semantic-affective space for each tweet. In addition, significant enhancements were made in the main system dealing with the data preprocessing and feature extraction including the employment of word embeddings. Each model is used to predict a tweet's sentiment (positive, negative or neutral) and a late fusion scheme is adopted for the final decision.

Original languageEnglish
Title of host publicationSemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages155-163
Number of pages9
ISBN (Electronic)9781941643952
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event10th International Workshop on Semantic Evaluation, SemEval 2016 - San Diego, United States
Duration: 16 Jun 201617 Jun 2016

Publication series

NameSemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings

Conference

Conference10th International Workshop on Semantic Evaluation, SemEval 2016
Country/TerritoryUnited States
CitySan Diego
Period16/06/1617/06/16

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