EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification

Maoquan Wang, Shiyun Chen, Yufei Xie, Lu Zhao


Abstract
This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet sentiment polarity, and (2) sentiment quantification, that is, estimating the sentiment distributions of a set of given tweets. We build a convolutional sentence classification system for the task of SAT. Official results show that the experimental results of our system are comparative.
Anthology ID:
S17-2124
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
737–740
Language:
URL:
https://www.aclweb.org/anthology/S17-2124
DOI:
10.18653/v1/S17-2124
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2124.pdf