Abstract

This paper presents a cross-language sentiment classification method that based on the Support Vector Machine model. The method is based on the research about English training corpus, first of all, we use statistical methods extract feature words in English, and use machine translation tools build an “English-Chinese” feature word bank. Then, we put forward a feature word weighting method which combined TF-IDF with sentimental intensity of sentiment words, after that, we constructed a vector space model. Finally, we optimized the Support Vector Machine classification model by using a joint training set. Experimental results show the effectiveness of this method.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.