Sentiment classification with polarity shifting detection

Shoushan Li, Zhongqing Wang, Yat Mei Lee, Chu-ren Huang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

20 Citations (Scopus)


Sentiment classification is now a hot research issue in the community of natural language processing and the bag-of-words based machine learning approach is the state-of-the-art for this task. However, one important phenomenon, called polarity shifting, remains unsolved in the bag-of-words model, which sometimes makes the machine learning approach fails. In this study, we aim to perform sentiment classification with full consideration of the polarity shifting phenomenon. First, we extract some detection rules for detecting polarity shifting of sentimental words from a corpus which consists of polarity-shifted sentences. Then, we use the detection rules to detect the polarity-shifted words in the testing data. Third, a novel term counting-based classifier is designed by fully considering those polarity-shifted words. Evaluation shows that the novel term counting-based classifier significantly improves the performance of sentiment analysis across five domains. Furthermore, when this classifier is combined with a machine-learning based classifier, the combined classifier yields better performance than either of them.
Original languageEnglish
Title of host publicationProceedings - 2013 International Conference on Asian Language Processing, IALP 2013
Number of pages4
Publication statusPublished - 1 Dec 2013
Event2013 International Conference on Asian Language Processing, IALP 2013 - Urumqi, Xinjiang, China
Duration: 17 Aug 201319 Aug 2013


Conference2013 International Conference on Asian Language Processing, IALP 2013
CityUrumqi, Xinjiang


  • emotion
  • semi-supervised learning
  • sentiment classification

ASJC Scopus subject areas

  • Software

Cite this