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Sentiment Classification by Combining Lexicon-based and Machine Learning Methods

Author: WangZhenHao
Tutor: DingYuXin
School: Harbin Institute of Technology
Course: Computer Science and Technology
Keywords: Sentiment classification Perspectives mining Text Classification Support Vector Machine TFIDF
CLC: TP391.1
Type: Master's thesis
Year: 2010
Downloads: 276
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Abstract


Recent years, with the vigorous development of e-commerce , personal blog , social networking sites and microblogging , the Internet has entered a new era of user-generated text (user generated content) generation , marked people is no longer a mere audience but has become a part of the Internet . The majority of users have the space to express their views , speech or evaluation along with subjective overwhelming . These vast amounts of unstructured information obviously contains a lot of information . Companies need to obtain the views of the users of the product , the government needs to know the mass of a particular policy reflects . Users want to get more suggestions before consumption . How to deal with this information we want to obtain knowledge , is the the scholars focus of attention . The emotion classification followed the rise of a field of study , it is the emotional expression of the text from the start of the text classification points , had a positive (positive) and negative ( negative) . So that we can know the attitude expressed by the text information in favor of or against this product worth recommending or worthless . In such a context , the problem of text sentiment classification following research : First of all, for the emotional classification features a self- supervised classification model , based sentiment classification the dictionary method based machine learning combination of methods to overcome based on the completeness of the dictionary method based on machine learning require substantial human-annotated training set ; Furthermore, this article attempts to information retrieval TFIDF model is introduced into the emotional category , be adjusted to adapt to the emotional classification problems . Finally, this paper build classification model engineering , experimental proof of sentiment classification on a common data set , the classification model presented in this paper can get the higher classification accuracy rate in the training set do not need a huge manual annotation case . The TFIDF the improved weighted model beable weighted model provides more information , and thus better classification results achieved Beable weighted model .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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