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

[How to correct problems with metadata yourself]


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
Editors:
Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
741–746
Language:
URL:
https://aclanthology.org/S17-2125
DOI:
10.18653/v1/S17-2125
Bibkey:
Cite (ACL):
Quanzhi Li, Armineh Nourbakhsh, Xiaomo Liu, Rui Fang, and Sameena Shah. 2017. funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 741–746, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts (Li et al., SemEval 2017)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/S17-2125.pdf