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Enriching Lexicons with Ephemeral Words for Sentiment Analysis in Social Streams

Published: 25 June 2018 Publication History

Abstract

Lexical approaches for sentiment analysis like SentiWordNet rely upon a fixed dictionary of words with fixed sentiment, i.e., sentiment that does not change. With the rise of Web 2.0 however, what we observe more and more often is that words that are not sentimental per se, are often associated with positive/negative feelings, for example, "refugees", "Trump", "iphone". Typically, those feelings are temporary as responses to external events; for example, "iphone" sentiment upon latest iphone version release or "Trump" sentiment after USA withdraw from Paris climate agreement.
In this work, we propose an approach for extracting and monitoring what we call ephemeral words from social streams; these are words that convey sentiment without being sentimental and their sentiment might change with time. Such sort of words cannot be part of a lexicon like SentiWordNet since their sentiment has an ephemeral character, however detecting such words and estimating their sentiment can significantly improve the performance of lexicon-based approaches, as our experiments show.

References

[1]
Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. In LREC, Vol. 10. 2200--2204.
[2]
Albert Bifet and Eibe Frank. 2010. Sentiment knowledge discovery in twitter streaming data. In International conference on discovery science. Springer, 1--15.
[3]
RoiBlanco, Giuseppe Ottaviano, and Edgar Meij. 2015. Fast and space-efficient entity linking for queries. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. ACM, 179--188.
[4]
Yan Dang, Yulei Zhang, and Hsinchun Chen. 2010. A lexicon-enhanced method for sentiment classification: An experiment on online product reviews. IEEE Intelligent Systems 25, 4 (2010), 46--53.
[5]
John Cristian Borges Gamboa. 2017. Deep Learning for Time-Series Analysis. arXiv preprint arXiv:1701.01887 ( 2017).
[6]
Alec Go, Richa Bhayani, and Lei Huang. 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford 1 (2009), 12.
[7]
Vasileios Iosifidis and Eirini Ntoutsi. 2017. Large Scale Sentiment Learning with Limited Labels. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1823--1832.
[8]
Vasileios Iosifidis, Annina Oelschlager, and Eirini Ntoutsi. 2017. Sentiment Classification over Opinionated Data Streams Through Informed Model Adaptation. In International Conference on Theory and Practice of Digital Libraries. Springer, 369--381.
[9]
Jussi Karlgren, Magnus Sahlgren, Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. 2012. Usefulness of sentiment analysis. In European Conference on Information Retrieval. Springer, 426--435.
[10]
Aamera ZH Khan, Mohammad Atique, and VM Thakare. 2015. Combining lexicon-based and learning-based methods for Twitter sentiment analysis. International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE) (2015), 89.
[11]
Farhan Hassan Khan, Usman Qamar, and Saba Bashir. 2016. SentiMI: Introducing point-wise mutual information with SentiWordNet to improve sentiment polarity detection. Applied Soft Computing 39 (2016), 140--153.
[12]
Atsutoshi Kumagai and Tomoharu Iwata. 2016. Learning future classifiers without additional data. In Thirtieth AAAI Conference on Artificial Intelligence.
[13]
Saif M Mohammad, Svetlana Kiritchenko, and Xiaodan Zhu. 2013. NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:1308.6242 (2013).
[14]
Bruno Ohana and Brendan Tierney. 2009. Sentiment classification of reviews using SentiWordNet. In 9th. IT & T Conference. 13.
[15]
Reynier Ortega, Adrian Fonseca, and Andres Montoyo. 2013. SSA-UO: unsupervised Twitter sentiment analysis. In Second joint conference on lexical and computational semantics (SEM), Vol. 2. 501--507.
[16]
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 79--86.
[17]
Hassan Saif, Miriam Fernandez, Yulan He, and Harith Alani. 2013. Evaluation Datasets for Twitter Sentiment Analysis A survey and a new dataset, the STS-Gold. (2013).
[18]
Hassan Saif, Miriam Fernandez, Yulan He, and Harith Alani. 2014. Senticircles for contextual and conceptual semantic sentiment analysis of twitter. In European Semantic Web Conference. Springer, 83--98.
[19]
Myra Spiliopoulou, Eirini Ntoutsi, and Max Zimmermann. 2016. Opinion Stream Mining. Springer US, Boston, MA, 1--10.
[20]
Duyu Tang, Furu Wei, Nan Yang, Ming Zhou, Ting Liu, and Bing Qin. 2014. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In ACL (1). 1555--1565.
[21]
Pablo A Tapia and Juan D Velásquez. 2014. Twitter sentiment polarity analysis: A novel approach for improving the automated labeling in a text corpora. In International Conference on Active Media Technology. Springer, 274--285.
[22]
Sebastian Wagner, Max Zimmermann, Eirini Ntoutsi, and Myra Spiliopoulou. 2015. Ageing-based multinomial naive bayes classifiers over opinionated data streams. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 401--416.

Cited By

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  • (2024)Leveraging Machine Translation to Enhance Sentiment Analysis on Multilingual TextProceedings of the 2024 13th International Conference on Software and Computer Applications10.1145/3651781.3651819(242-248)Online publication date: 1-Feb-2024
  • (2020)Sentiment analysis on big sparse data streams with limited labelsKnowledge and Information Systems10.1007/s10115-019-01392-962:4(1393-1432)Online publication date: 1-Apr-2020
  • (2018)Learning under Feature Drifts in Textual StreamsProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271717(527-536)Online publication date: 17-Oct-2018

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  1. Enriching Lexicons with Ephemeral Words for Sentiment Analysis in Social Streams

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      cover image ACM Other conferences
      WIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics
      June 2018
      398 pages
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      Published: 25 June 2018

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      Author Tags

      1. dictionary-based approaches
      2. ephemeral words
      3. lexicon enrichment
      4. sentiment classification

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      View all
      • (2024)Leveraging Machine Translation to Enhance Sentiment Analysis on Multilingual TextProceedings of the 2024 13th International Conference on Software and Computer Applications10.1145/3651781.3651819(242-248)Online publication date: 1-Feb-2024
      • (2020)Sentiment analysis on big sparse data streams with limited labelsKnowledge and Information Systems10.1007/s10115-019-01392-962:4(1393-1432)Online publication date: 1-Apr-2020
      • (2018)Learning under Feature Drifts in Textual StreamsProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271717(527-536)Online publication date: 17-Oct-2018

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