TY - GEN
T1 - ECNU
T2 - 9th International Workshop on Semantic Evaluation, SemEval 2015 co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015
AU - Zhang, Zhihua
AU - Wu, Guoshun
AU - Lan, Man
N1 - Publisher Copyright:
© 2015 Association for Computational Linguistics
PY - 2015
Y1 - 2015
N2 - This paper reports our submission to task 10 (Sentiment Analysis on Tweet, SAT) (Rosenthal et al., 2015) in SemEval 2015, which contains five subtasks, i.e., contextual polarity disambiguation (subtask A: expression-level), message polarity classification (subtask B: message-level), topic-based message polarity classification and detecting trends towards a topic (subtask C and D: topic-level), and determining sentiment strength of twitter terms (subtask E: term-level). For the first four subtasks, we built supervised models using traditional features and word embedding features to perform sentiment polarity classification. For subtask E, we first expanded the training data with the aid of external sentiment lexicons and then built a regression model to estimate the sentiment strength. Despite the simplicity of features, our systems rank above the average.
AB - This paper reports our submission to task 10 (Sentiment Analysis on Tweet, SAT) (Rosenthal et al., 2015) in SemEval 2015, which contains five subtasks, i.e., contextual polarity disambiguation (subtask A: expression-level), message polarity classification (subtask B: message-level), topic-based message polarity classification and detecting trends towards a topic (subtask C and D: topic-level), and determining sentiment strength of twitter terms (subtask E: term-level). For the first four subtasks, we built supervised models using traditional features and word embedding features to perform sentiment polarity classification. For subtask E, we first expanded the training data with the aid of external sentiment lexicons and then built a regression model to estimate the sentiment strength. Despite the simplicity of features, our systems rank above the average.
UR - https://www.scopus.com/pages/publications/85122015485
U2 - 10.18653/v1/s15-2094
DO - 10.18653/v1/s15-2094
M3 - 会议稿件
AN - SCOPUS:85122015485
T3 - SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings
SP - 561
EP - 567
BT - SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics
A2 - Nakov, Preslav
A2 - Zesch, Torsten
A2 - Cer, Daniel
A2 - Jurgens, David
PB - Association for Computational Linguistics (ACL)
Y2 - 4 June 2015 through 5 June 2015
ER -