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Emotion classifying method fusing intrinsic feature and shallow feature

A technology of emotion classification and deep features, applied in the field of emotion classification, can solve problems such as ignoring semantic relations, achieve the effect of improving classification performance and increasing accuracy

Active Publication Date: 2016-08-03
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

When using the TF-IDF method to represent text features, each dimension of text features represents a fixed word in the text. Although the feature representation of a single word is very clear, it ignores the semantic relationship between words

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  • Emotion classifying method fusing intrinsic feature and shallow feature
  • Emotion classifying method fusing intrinsic feature and shallow feature
  • Emotion classifying method fusing intrinsic feature and shallow feature

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Embodiment Construction

[0025] Below in conjunction with accompanying drawing, the present invention will be further described:

[0026] Such as figure 1 Shown, the specific steps of the emotional classification method of the present invention fusion deep layer and shallow feature are:

[0027]Step 1: Collect emotional text corpus from the Internet, and manually mark the categories, such as the text label of positive emotion is 1, and the text label of negative emotion is 2. And remove the leading and trailing spaces of the text, and represent the data in the text as a sentence, which is convenient for subsequent processing. And the corpus is divided into training set and test set. The training set is used to train the sentiment classification model, and the test set is used to test the classification effect of the model.

[0028] Step 2: First, collect sentiment dictionaries from the Internet. Sentiment dictionaries are the basic resources for text sentiment analysis, and are actually a collectio...

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Abstract

The invention discloses an emotion classifying method fusing an intrinsic feature and a shallow feature. The emotion classifying method is characterized in that the intrinsic feature of fusion Doc2vec and the shallow feature of TF-IDF are used for representing features of a text. By adopting a fusion method, the problem of unclear expression of a fixed word feature in the Doc2vec is solved, the problem that semantics among words is not considered in the TF-IDF method is also solved, and the expression of a text vector specific to the text is clearer. An SVM classifying method is adopted, so that better classifying performance of a classifier is achieved. The method is used for solving an emotion classifying problem, so that the emotion classifying accuracy can be improved remarkably.

Description

technical field [0001] The invention belongs to an emotion classification method, in particular to an emotion classification method integrating deep features and shallow features. Background technique [0002] Sentiment analysis is a common application of natural language processing (NLP) methods, especially in classification methods aimed at extracting the emotional content of text. Sentiment classification has many useful practices, such as companies analyzing consumer feedback on products, or detecting negative reviews in online reviews. [0003] The vector representation of emotional text generally has two expressions, One-hotRepresentation and DistributedRepresentation. The biggest problem with One-hot Representation is that it cannot analyze the semantic relationship between words and words. In addition, this method is also prone to the disaster of dimensionality. The DistributedRepresentation method overcomes these shortcomings well, and word2vec is a typical repres...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/35G06F16/374
Inventor 唐贤伦周冲周家林白银刘想德张毅马艺玮
Owner CHONGQING UNIV OF POSTS & TELECOMM
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