Topic mining sentiment analysis method based on user feature optimization

Abstract

The invention belongs to sentiment analysis and theme mining tasks in the field of natural language processing, and particularly relates to a theme mining sentiment analysis method based on user characteristic optimization. The method comprises: S1, establishing a multi-dimensional theme and emotion joint model MTSM based on an LDA theme model, and text information, time, user characteristics andemotion tags are fused in the model; S2, utilizing the training corpus to train a model, and solving model parameters; and S3, utilizing the trained model to carry out topic mining and emotion prediction on the test corpus. The invention aims at the characteristics of a network social text. The method has the advantages that text information, time, user characteristics, emotion tags and other four-dimensional information are effectively integrated, a network social text generation mode is redefined, a multi-dimensional topic emotion score combination type is established, topic information is observed and compared from multiple perspectives, and the emotion prediction accuracy of the network social text is improved.

Description

technical field [0001] The invention belongs to the task of sentiment analysis and topic mining in the field of natural language processing, and more specifically relates to a topic mining sentiment analysis method based on user feature optimization. Background technique [0002] Internet social network text contains users' opinions and personal emotions. The process of extracting this unstructured network data is called sentiment analysis or opinion mining. According to the basic attributes of the methods, they can be mainly divided into machine learning models, dictionary-based learning models, and topic models. In recent years, due to the vigorous development of topic models, a large number of topic-based models have been extended to sentiment prediction and classification models, and the field of sentiment analysis is used to do sentiment classification work on Internet user-generated texts, for example, the sentiment of product review information and movie review inform...

AI Technical Summary

Problems solved by technology

But so far, no relevant research work has proposed how to effectively integrate users' multi-dimensional features, time, text, and emotional tags into topic models.

Images