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Research and Realization Based on Subjective Objective Text Classification Preprocessing Methods

Author: ZhangXiaoKai
Tutor: YaoTian
School: Shanghai Jiaotong University
Course: Applied Computer Technology
Keywords: Text Classification Data Mining Naive Bayes Support Vector Machine Non - specification language Pattern Matching Feature extraction
CLC: TP391.1
Type: Master's thesis
Year: 2009
Downloads: 50
Quote: 5
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Abstract


With the popularity of the Internet, the network has become a very important tool for people to obtain information. However, with the information explosion-like growth, it is difficult to be found within a short period of time they need information, which is information overload problem. Text classification is an important research direction of data mining. Some applications work, such as the evaluation of e-commerce, the results of the opinion poll text mining. However, from the flood of information manually find the views of subjectivity text is unrealistic. In this paper, we propose a method for the subjective and objective text classification. Is extracted by analysis of the differences existing between the subjective and objective text to be able to distinguish between some of their characteristics. The final application Naive Bayes and support vector machine model with different combinations of feature items on its subjective and objective text categorization, and strive to achieve optimal results. With the popularity of instant messaging (Instant Messaging) software (such as MSN, QQ, etc.), a non-specification language widely appeared in. Special language used in these environments is known as a network of non-specification language (Network Informal Language, NIL) expression. For example, commonly used in Internet chat \Traditional text mining, these information are regarded as noise. But in fact, the sentence that contains these non-normative words often there is information of the user's express wishes of the individual. For example, \Express through words holders own views and opinions on certain models. In the present work, the text preprocessing research is to normalize these subjective text. First, through the preparation of a specific web spider program to collect a certain period of time on a forum page. Page manual screening, to build specification language dictionary. Non-canonical word common on the Internet will eventually be divided into six major categories. Taking into account the cost of processing, these six broad categories is divided into two major categories: the typical specification language and ambiguity of non-specification language. For the typical specification language, the use of its coverage algorithm based on sequence pattern matching method normalize. For ambiguous non-specification language, the word of this category is difficult to determine whether a non-word level specification. Classification method based on the feature extraction to identify. Ultimately achieve the purpose of regularization. Draw basic formal subjective texts.

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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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