Text sentiment classification method based on deep learning and feature fusion

Abstract

The invention provides a text sentiment classification method based on deep learning and feature fusion, which learns text representation by integrating hierarchical features and comprises the following steps: designing vector representation of different environments for sentiment words according to text polarity and negative words; performing part-of-speech sampling to obtain vector distinguishing homomorphic words; optimizing word vectors in combination with emoticons, and learning word vector-based simple sentence features by using a neural network model; learning word vector-based simple sentence features through a word sequence; splicing the two parts to obtain sentence-level features; for a document at least containing two simple sentences, inputting a sentence vector sequence\ intoan upper-layer neural network to learn document features based on the neural network, segment heads, averaging segment tails and sentences containing summary words to obtain document features based onrules, and the two parts are spliced to obtain document-level features;nd for a specific task, inputting the single sentence or document features into the random forest classifier to predict the emotion category. Compared with a basic model, the accuracy of text sentiment classification can be effectively improved.

Description

technical field [0001] The invention relates to an emotion classification method, in particular to a text emotion classification method based on deep learning and feature fusion, and belongs to the technical field of natural language processing. Background technique [0002] Text sentiment analysis aims to automatically identify subjective text from unstructured text, and can be applied to social media analysis, automatic machine question answering and other fields. With the development of the Internet and the rise of social media (such as Weibo, Twitter, Facebook, and IMDB), more and more users have gradually transformed from information receivers to information contributors, and the subjective texts on the Internet are rapidly changing. increase. These massive user-generated texts provide a very convenient platform for analyzing user sentiment, but also bring many challenges to text sentiment analysis. The significance of text sentiment analysis can be summarized as foll...

AI Technical Summary

Problems solved by technology

In addition, traditional discrete features usually have high dimensions, and it is difficult to complete the semantic combination and semantic understanding of text under such a sparse 0 / 1 feature representation

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