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Generating domain-specific affective ontology from Chinese reviews for sentiment analysis

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Abstract

Considering the diversities and ambiguities of opinion expressions in Chinese online product reviews, normal sentiment analysis technologies have exposed their inadequateness in both classification accuracy and identifying effectiveness. We propose a novel approach which can easily identify product features and corresponding opinions by building a domain-specific affective ontology and thus mapping comment sentences to the objects defined in the affective ontology. Ontology is created automatically by processing the online reviews; both product features and affective words are presented as nodes which are connected to each other by their semantic relationship. Furthermore, in order to increase the accuracy, we introduce a dynamic polarity detection technique for affective words whose sentimental tendencies are dependent on particular contexts. The experimental results clearly demonstrate the performance improvement of our approach compared with others in real world online product reviews for classification tests.

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Authors and Affiliations

  1. College of Information Engineering, Capital Normal University, Beijing, 100048, China

    Li-zhen Liu  (刘丽珍), Hao Liu  (刘 昊), Han-shi Wang  (王函石), Wei Song  (宋 巍) & Xin-lei Zhao  (赵新蕾)

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  1. Li-zhen Liu  (刘丽珍)
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  2. Hao Liu  (刘 昊)
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  3. Han-shi Wang  (王函石)
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  4. Wei Song  (宋 巍)
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  5. Xin-lei Zhao  (赵新蕾)
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Corresponding author

Correspondence to Han-shi Wang  (王函石).

Additional information

Foundation item: the National Natural Science Foundation of China (Nos. 61303105 and 61402304), the Humanity & Social Science General Project of Ministry of Education (No. 14YJAZH046), the Beijing Natural Science Foundation (No. 4154065), the Beijing Educational Committee Science and Technology Development Plan (No. KM201410028017) and the Academic Degree Graduate Courses Group Projects

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Liu, Lz., Liu, H., Wang, Hs. et al. Generating domain-specific affective ontology from Chinese reviews for sentiment analysis. J. Shanghai Jiaotong Univ. (Sci.) 20, 32–37 (2015). https://doi.org/10.1007/s12204-015-1584-0

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  • DOI: https://doi.org/10.1007/s12204-015-1584-0

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