Text sentiment classification method facing Chinese Web comments
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A technology for sentiment classification and text, applied in the field of data processing
Inactive Publication Date: 2013-05-22
WUXI NANLIGONG TECH DEV +1
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But at present, neither English nor Chinese has a complete dictionary covering the semantic orientation of words, and it ...
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Embodiment 1
[0035] The overall process of text sentiment classification is as follows: figure 1 shown. The whole process can be divided into two parts: training process and classification process.
[0036] The basic flow of the training process is: training text preprocessing → feature selection → vectorized representation of text → training classifier. The specific processing is as follows:
[0037] 1. Given a manually classified training text set , to perform some preprocessing on it, such as Chinese word segmentation, stop word filtering, etc.
[0038] 2. Use statistics such as frequency to calculate the category of entries in the text The distribution in , after feature selection, get the local features of this category. Set the set of selected feature words ,in for category in the first feature words, Indicates the total number of feature words in this category. The union of the local feature word sets of all categories The set of global feature words that constitu...
Embodiment 2
[0052] Embodiment 2, vector space model
[0053] The Vector Space Model (Vector Space Model, VSM), proposed by Salton et al. of Harvard University in 1975, was first applied as an indexing method.
[0054] The basic idea of VSM is to use Bag of words (Bow) to represent text, and each entry is used as one dimension of the feature space coordinate system, and the text is regarded as a vector of feature space, and the angle between the two vectors is used to measure the similarity between two texts.
[0055] In VSM, each document is mapped to a point in a vector space spanned by a set of canonically orthogonalized feature vectors. Assuming that the set consisting of n feature items is F=(t1,t2,...,tn), the document is formalized as a vector di=(wi1,wi2,...,wik,...,win ), wik represents the weight of the kth feature item entry tk of di. The value of each dimension of the vector represents the weight of the feature item in the document, which is used to describe the importance...
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Abstract
The invention belongs to the field of data processing technology and discloses a text sentiment classification method facing Chinese Web comments. The text sentiment classification method includes a training process and a classification process. The training process includes the steps of carrying out training text preprocessing, carrying out feature selecting, carrying out vectorization representation of a text and obtaining a training classifier. The classification process includes the steps of carrying out test text preprocessing, carrying out feature selecting, utilizing the classifier to classify and outputting a classification result. On the basis of an original document classification method, document frequency (DF) and information gain (IG) are used and a sentiment dictionary of negative words, degree adverbs and dynamic sentiment words are built to distinguish sentiment tendency of Chinese feature words, select feature words, calculate a feature weight value and build a feature vector. Moreover, a NaiveBayes classification algorithm is used for training to obtain the classifier, carrying out sentiment classification on the text, providing effective data mining for users and then carrying out analysis processing.
Description
[0001] technical field [0002] The invention belongs to the technical field of data processing, and in particular relates to a text emotion classification method for Chinese Web comments. Background technique [0003] As an important information interaction medium, the main function of text is to express emotions. Content-based research has been very mature. In recent years, more and more studies have begun to focus on "expression", that is, sentiment analysis. The main research contents include Semantic orientation recognition of words, sentiment-based text classification, opinion extraction, subjectivity analysis, etc. For a document, the main thing that can play a decisive role in its semantic orientation is the words used to make up the document. Therefore, the basis of sentiment-based text classification is to determine the semantic orientation of words. But at present, neither English nor Chinese has a complete dictionary covering the semantic orientation of words, ...
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