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A valences-totaling model for English sentiment classification

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Abstract

Sentiment classification plays an important role in everyday life, in political activities, activities of commodity production and commercial activities. Finding a time-effective and highly accurate solution to the classification of emotions is challenging. Today, there are many models (or methods) to classify the sentiment of documents. Sentiment classification has been studied for many years and is used widely in many different fields. We propose a new model, which is called the valences-totaling model (VTM), by using cosine measure (CM) to classify the sentiment of English documents. VTM is a new model for English sentiment classification. In this study, CM is a measure of similarity between two words and is used to calculate the valence (and polarity) of English semantic lexicons. We prove that CM is able to identify the sentiment valence and the sentiment polarity of the English sentiment lexicons online in combination with the Google search engine with AND operator and OR operator. VTM uses many English semantic lexicons. These English sentiment lexicons are calculated online and are based on the Internet. We present a full range of English sentences; thus, the emotion expressed in the English text is classified with more precision. Our new model is not dependent on a special domain and training data set—it is a domain-independent classifier. We test our new model on the Internet data in English. The calculated valence (and polarity) of English semantic words in this model is based on many documents on millions of English Web sites and English social networks.

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam

    Vo Ngoc Phu

  2. Computer Science and Engineering (CSE), Ho Chi Minh City University of Technology (HCMUT), Vietnam National University, Ho Chi Minh City, Vietnam

    Vo Thi Ngoc Chau

  3. Faculty of Information Technology, Ly Tu Trong Technical College, Ho Chi Minh City, Vietnam

    Nguyen Duy Dat

  4. School of Industrial Management (SIM), Ho Chi Minh City University of Technology (HCMUT), Vietnam National University, Ho Chi Minh City, Vietnam

    Vo Thi Ngoc Tran

  5. Faculty of Computer Networks and Communications, University of Information Technology (UIT), Vietnam National University of Hochiminh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Tuan A. Nguyen

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  1. Vo Ngoc Phu
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  2. Vo Thi Ngoc Chau
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  3. Nguyen Duy Dat
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  4. Vo Thi Ngoc Tran
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Correspondence to Vo Ngoc Phu.

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Phu, V.N., Chau, V.T.N., Dat, N.D. et al. A valences-totaling model for English sentiment classification. Knowl Inf Syst 53, 579–636 (2017). https://doi.org/10.1007/s10115-017-1054-0

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  • DOI: https://doi.org/10.1007/s10115-017-1054-0

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