Emotion classification method based on part-of-speech combination and feature selection

Abstract

The invention discloses an emotion classification method based on part-of-speech combination and feature selection. The method comprises the following steps of firstly, initializing word-part-of-speech Word2vec model; secondly, carrying out preprocessing operation on data, and selecting feature words with emotion information from preprocessed data based on an emotion dictionary; thirdly, combiningthe feature words and the part-of-speech of texts, and converting the texts into word and part-of-speech pair sequence texts; fourthly, obtaining vectors of the feature words of the word and part-of-speech pair sequence texts through the word-part-of-speech Word2vec model, and performing addition and averaging on the vectors of the words according to the dimensions for the texts to represent thetexts, thereby obtaining eigenvectors of the texts; and finally, obtaining an emotion classification model by utilizing an SVM classifier. The method has the beneficial effects that the emotion dictionary is used for extracting the feature words, and the feature words with the single emotion information are highlighted; and on the other hand, a phrase structure of emotional tendency is extracted based on phrase structure optimization and word segmentation, and the words and the part-of-speech are combined to solve the problem that one word has multiple meanings.

Description

technical field [0001] The invention relates to the field of computer science, in particular to an emotion classification method based on part-of-speech combination and feature selection. Background technique [0002] With the rapid development of social networking platforms, especially Weibo, a large number of netizens can express their opinions and emotions on social events more conveniently, resulting in a large amount of Weibo comment data, which contains rich opinions and views. Emotional information, how to deeply analyze and mine the emotional tendency of the massive data of Weibo texts has become a hot research direction. Traditional sentiment classification methods only focus on lexical features and syntactic features, ignoring the semantic features between words. [0003] Although the word vector model trained by traditional Word2vec can reflect the potential semantic relationship between words, there are often some problems when training the model. First, the Wor...

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Problems solved by technology

[0003] Although the word vector model trained by traditional Word2vec can reflect the potential semantic relationship between words, there are often some problems when training the model. First, the Word2vec tool cannot directly extract the phrase structure that better reflects the emotional tendency of the text. For example, "unhappy" is divided into "no" and "happy". Word2vec learns the contextual semantics of the words "no" and "happy" during training, and cannot directly learn the vector of the phrase "unhappy".

The second is that it is impossible to distinguish the semantics of the same word under different parts of speech. For example, "Xiao Ming bought a bundle of incense and used it for sacrifices, but the incense I bought this time is too rubbish" and "The rice cooked by Xiao Ming is really fragrant", in the previous sentence "Xiang" in "Xiang" is a noun, which refers to the thin strips made of sawdust mixed with spices used in worshiping ancestors or worshiping gods. It has no emotional color and is a neutral word; Smell is a compliment

[0004] Traditional data storage and processing methods greatly waste computer resources and time

Moreover, due to its step-by-step processing mechanism, the traditional Hadoop cluster limits its performance efficiency, and the I / O overhead for the disk is extremely high.

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