UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015

Xu Han; Binyang Li; Jing Ma; Yuxiao Zhang; Gaoyan Ou; Tengjiao Wang; Kam Fai Wong

doi:10.18653/v1/S15-2111

UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015

Xu Han
, Binyang Li^*
, Jing Ma
, Yuxiao Zhang
, Gaoyan Ou
, Tengjiao Wang
, Kam Fai Wong

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

Microblogs are considered as We-Media information with many real-time opinions. This paper presents a Twitter-OpinMiner system for Twitter sentiment analysis evaluation at SemEval 2015. Our approach stems from two different angles: topic detection for discovering the sentiment distribution on different topics and sentiment analysis based on a variety of features. Moreover, we also implemented intra-sentence discourse relations for polarity identification. We divided the discourse relations into 4 predefined categories, including continuation, contrast, condition, and cause. These relations could facilitate us to eliminate polarity ambiguities in compound sentences where both positive and negative sentiments are appearing. Based on the SemEval 2014 and SemEval 2015 Twitter sentiment analysis task datasets, the experimental results show that the performance of Twitter-OpinMiner could effectively recognize opinionated messages and identify the polarities.

Original language	English
Title of host publication	Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
Editors	Preslav Nakov, Torsten Zesch, Daniel Cer, David Jurgens
Publisher	Association for Computational Linguistics (ACL)
Pages	664-668
Number of pages	5
ISBN (Electronic)	9781941643402
DOIs	https://doi.org/10.18653/v1/S15-2111
Publication status	Published - Jun 2015
Event	9th International Workshop on Semantic Evaluation, SemEval 2015 - Denver, United States Duration: 4 Jun 2015 → 5 Jun 2015

Publication series

Name	Proceedings of the International Workshop on Semantic Evaluation

Conference

Conference	9th International Workshop on Semantic Evaluation, SemEval 2015
Country/Territory	United States
City	Denver
Period	4/06/15 → 5/06/15

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10.18653/v1/S15-2111

Cite this

Han, X., Li, B., Ma, J., Zhang, Y., Ou, G., Wang, T., & Wong, K. F. (2015). UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015. In P. Nakov, T. Zesch, D. Cer, & D. Jurgens (Eds.), Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) (pp. 664-668). (Proceedings of the International Workshop on Semantic Evaluation). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/S15-2111

Han, Xu ; Li, Binyang ; Ma, Jing et al. / UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). editor / Preslav Nakov ; Torsten Zesch ; Daniel Cer ; David Jurgens. Association for Computational Linguistics (ACL), 2015. pp. 664-668 (Proceedings of the International Workshop on Semantic Evaluation).

@inproceedings{4855eeedae9f4947a3134e44cc7edf99,

title = "UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015",

abstract = "Microblogs are considered as We-Media information with many real-time opinions. This paper presents a Twitter-OpinMiner system for Twitter sentiment analysis evaluation at SemEval 2015. Our approach stems from two different angles: topic detection for discovering the sentiment distribution on different topics and sentiment analysis based on a variety of features. Moreover, we also implemented intra-sentence discourse relations for polarity identification. We divided the discourse relations into 4 predefined categories, including continuation, contrast, condition, and cause. These relations could facilitate us to eliminate polarity ambiguities in compound sentences where both positive and negative sentiments are appearing. Based on the SemEval 2014 and SemEval 2015 Twitter sentiment analysis task datasets, the experimental results show that the performance of Twitter-OpinMiner could effectively recognize opinionated messages and identify the polarities.",

author = "Xu Han and Binyang Li and Jing Ma and Yuxiao Zhang and Gaoyan Ou and Tengjiao Wang and Wong, \{Kam Fai\}",

note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics; 9th International Workshop on Semantic Evaluation, SemEval 2015 ; Conference date: 04-06-2015 Through 05-06-2015",

year = "2015",

month = jun,

doi = "10.18653/v1/S15-2111",

language = "English",

series = "Proceedings of the International Workshop on Semantic Evaluation",

publisher = "Association for Computational Linguistics (ACL)",

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editor = "Preslav Nakov and Torsten Zesch and Daniel Cer and David Jurgens",

booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)",

address = "Australia",

}

Han, X, Li, B, Ma, J, Zhang, Y, Ou, G, Wang, T & Wong, KF 2015, UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015. in P Nakov, T Zesch, D Cer & D Jurgens (eds), Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). Proceedings of the International Workshop on Semantic Evaluation, Association for Computational Linguistics (ACL), pp. 664-668, 9th International Workshop on Semantic Evaluation, SemEval 2015, Denver, United States, 4/06/15. https://doi.org/10.18653/v1/S15-2111

UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015. / Han, Xu; Li, Binyang; Ma, Jing et al.
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). ed. / Preslav Nakov; Torsten Zesch; Daniel Cer; David Jurgens. Association for Computational Linguistics (ACL), 2015. p. 664-668 (Proceedings of the International Workshop on Semantic Evaluation).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Han, Xu

AU - Li, Binyang

AU - Ma, Jing

AU - Zhang, Yuxiao

AU - Ou, Gaoyan

AU - Wang, Tengjiao

AU - Wong, Kam Fai

PY - 2015/6

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N2 - Microblogs are considered as We-Media information with many real-time opinions. This paper presents a Twitter-OpinMiner system for Twitter sentiment analysis evaluation at SemEval 2015. Our approach stems from two different angles: topic detection for discovering the sentiment distribution on different topics and sentiment analysis based on a variety of features. Moreover, we also implemented intra-sentence discourse relations for polarity identification. We divided the discourse relations into 4 predefined categories, including continuation, contrast, condition, and cause. These relations could facilitate us to eliminate polarity ambiguities in compound sentences where both positive and negative sentiments are appearing. Based on the SemEval 2014 and SemEval 2015 Twitter sentiment analysis task datasets, the experimental results show that the performance of Twitter-OpinMiner could effectively recognize opinionated messages and identify the polarities.

AB - Microblogs are considered as We-Media information with many real-time opinions. This paper presents a Twitter-OpinMiner system for Twitter sentiment analysis evaluation at SemEval 2015. Our approach stems from two different angles: topic detection for discovering the sentiment distribution on different topics and sentiment analysis based on a variety of features. Moreover, we also implemented intra-sentence discourse relations for polarity identification. We divided the discourse relations into 4 predefined categories, including continuation, contrast, condition, and cause. These relations could facilitate us to eliminate polarity ambiguities in compound sentences where both positive and negative sentiments are appearing. Based on the SemEval 2014 and SemEval 2015 Twitter sentiment analysis task datasets, the experimental results show that the performance of Twitter-OpinMiner could effectively recognize opinionated messages and identify the polarities.

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Han X, Li B, Ma J, Zhang Y, Ou G, Wang T et al. UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015. In Nakov P, Zesch T, Cer D, Jurgens D, editors, Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). Association for Computational Linguistics (ACL). 2015. p. 664-668. (Proceedings of the International Workshop on Semantic Evaluation). doi: 10.18653/v1/S15-2111

UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015

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