Abstract
In recent years, online sentiment texts are generated by users in various domains and in different languages. Binary polarity classification (positive or negative) on business sentiment texts can help both companies and customers to evaluate products or services. Sometimes, the polarity of sentiment texts can be modified, making the polarity classification difficult. In sentiment analysis, such modification of polarity is termed as polarity shifting, which shifts the polarity of a sentiment clue (emotion, evaluation, etc.). It is well known that detection of polarity shifting can help improve sentiment analysis in texts. However, to detect polarity shifting in corpora is challenging: (1) polarity shifting is normally sparse in texts, making human annotation difficult; (2) corpora with dense polarity shifting are few; we may need polarity shifting patterns from various corpora.
In this article, an approach is presented to extract polarity shifting patterns from any text corpus. For the first time, we proposed to select texts rich in polarity shifting by the idea of natural annotation, which is used to replace human annotation. With a sequence mining algorithm, the selected texts are used to generate polarity shifting pattern candidates, and then we rank them by C-value before human annotation. The approach is tested on different corpora and different languages. The results show that our approach can capture various types of polarity shifting patterns, and some patterns are unique to specific corpora. Therefore, for better performance, it is reasonable to construct polarity shifting patterns directly from the given corpus.
- Ellen S. Berscheid. 1978. The Pollyanna principle: Selectivity in language, memory, and thought. Psyccritiques 26, 8 (1978).Google Scholar
- Noemi Boubel, Thomas Francois, and Hubert Naets. 2013. Automatic extraction of contextual valence shifters. In Proceedings of the Conference on Recent Advances in Natural Language Processing (RANLP’13). 98--104.Google Scholar
- Mihaela Colhon, Madalina Cerban, Alex Becheru, and Mirela Teodorescu. 2016. Polarity shifting for Romanian sentiment classification. In Proceedings of the International Symposium on Innovations in Intelligent Systems and Applications. 1--6.Google Scholar
Cross Ref
- Katerina Frantzi, Sophia Ananiadou, and Hideki Mima. 2000. Automatic recognition of multi-word terms: The C-value/NC-value method. Int. J. Dig. Lib. 3, 2 (2000), 115--130.Google Scholar
Cross Ref
- Daisuke Ikeda, Hiroya Takamura, LevArie. Ratinov, and Manabu Okumura. 2008. Learning to shift the polarity of words for sentiment classification. In Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP’08). 296--303.Google Scholar
- J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. 2001. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of the International Conference on Data Engineering (ICDE’01). 215--224.Google Scholar
- A. Kennedy and D. Inkpen. 2006. Sentiment classification of movie reviews using contextual valence shifters. In Computational Intelligence 22, 2 (2006), 110--125.Google Scholar
Cross Ref
- Shoushan Li, Sophia Yat Mei Lee, and Chu-Ren Huang. 2013. Corpus construction on polarity shifting in sentiment analysis. In Chinese Lexical Semantics. Springer, 625--634.Google Scholar
- Shoushan Li, Sophia Yat Mei Lee, Chen Ying, Chu-Ren Huang, and Zhou Guodong. 2010. Sentiment classification and polarity shifting. In Proceedings of the International Conference on Computational Linguistics (COLING’10). 635--643.Google Scholar
- Shoushan Li, Zhongqing Wang, Sophia Yat Mei Lee, and Chu-Ren Huang. 2013. Sentiment classification with polarity shifting detection. In Proceedings of the International Conference on Asian Language Processing (IALP’13). IEEE, 129--132.Google Scholar
Digital Library
- Polanyi Livia and Zaenen Annie. 2004. Contextual lexical valence shifters. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications.Google Scholar
- Bo Pang and Lillian Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the Association for Computational Linguistics Conference (ACL’04). 271--278.Google Scholar
Digital Library
- Mukta Y. Raut and Mayura A. Kulkarni. 2017. Polarity shift in opinion mining. In Proceedings of the IEEE International Conference on Advances in Electronics, Communication and Computer Technology. 333--337.Google Scholar
- Marc Schulder, Michael Wiegand, and Josef Ruppenhofer. 2018. Automatically creating a lexicon of verbal polarity shifters: Mono- and cross-lingual methods for German. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, 2516--2528.Google Scholar
- Marc Schulder, Michael Wiegand, Josef Ruppenhofer, and Stephanie Koser. 2018. Introducing a lexicon of verbal polarity shifters for English. In Proceedings of the International Conference on Language Resources and Evaluation (LREC’18). 1393--1397.Google Scholar
- Marc Schulder, Michael Wiegand, Josef Ruppenhofer, and Benjamin Roth. 2017. Towards bootstrapping a polarity shifter lexicon using linguistic features. In Proceedings of the 8th International Joint Conference on Natural Language Processing. Asian Federation of Natural Language Processing, 624--633.Google Scholar
- Philip J. Stone. 1966. The General Inquirer: A Computer Approach to Content Analysis. The MIT Press.Google Scholar
- Maosong Sun. 2011. Natural language processing based on naturally annotated web resources. J. Chinese Inform. Proc. 8202, 4 (2011), 9--43.Google Scholar
- Michael Wiegand, Alexandra Balahur, Benjamin Roth, Dietrich Klakow, and Andrés Montoyo. 2010. A survey on the role of negation in sentiment analysis. In Proceedings of the Workshop on Negation and Speculation in Natural Language Processing. Association for Computational Linguistics, 60--68.Google Scholar
Digital Library
- Ge Xu, Churen Huang, and Houfeng Wang. 2013. Automatically Predicting the Polarity of Chinese Adjectives: Not, a Bit and a Search Engine. Springer Berlin. 453--465 pages.Google Scholar
- Ge Xu and Chu Ren Huang. 2016. Extracting Chinese polarity shifting patterns from massive text corpora. Lingua Sinica 2, 1 (2016), 5.Google Scholar
Cross Ref
Index Terms
Extracting Polarity Shifting Patterns from Any Corpus Based on Natural Annotation
Recommendations
Polarity Shifting: Corpus Construction and Analysis
IALP '11: Proceedings of the 2011 International Conference on Asian Language ProcessingPolarity shifting has been a challenge to automatic sentiment classification. In this paper, we create a corpus which consists of polarity-shifted sentences in various kinds of product reviews. In the corpus, both the sentimental words and shifting ...
A semantic approach based on domain knowledge for polarity shift detection using distant supervision
AbstractOne of the main challenges in sentiment analysis is the polarity shift. Studies have shown that the detection of polarity shifts is very effective to improve the accuracy of sentiment analysis. However, the problem of polarity shift detection has ...
Polarity shift detection, elimination and ensemble
The polarity shift problem is a major factor that affects classification performance of machine-learning-based sentiment analysis systems. In this paper, we propose a three-stage cascade model to address the polarity shift problem in the context of ...






Comments