funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts

Quanzhi Li, Armineh Nourbakhsh, Xiaomo Liu, Rui Fang, Sameena Shah


Abstract
This paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter. Topic-based (target-dependent) sentiment analysis has become attractive and been used in some applications recently, but it is still a challenging research task. In our approach, we take the left and right context of a target into consideration when generating polarity classification features. We use two types of word embeddings in our classifiers: the general word embeddings learned from 200 million tweets, and sentiment-specific word embeddings learned from 10 million tweets using distance supervision. We also incorporate a text feature model in our algorithm. This model produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method. We participated in four subtasks (B, C, D & E for English), all of which are about topic-based message polarity classification. Our team is ranked #6 in subtask B, #3 by MAEu and #9 by MAEm in subtask C, #3 using RAE and #6 using KLD in subtask D, and #3 in subtask E.
Anthology ID:
S17-2125
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:
741–746
Language:
URL:
https://www.aclweb.org/anthology/S17-2125
DOI:
10.18653/v1/S17-2125
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2125.pdf