Open AccessResearch on the Emotional Polarity Classification of Barrage Texts
Author(s) -
S. Qian,
Wenan Tan,
Lu Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2010/1/012012
Subject(s) - support vector machine , ambiguity , computer science , artificial intelligence , recall , classifier (uml) , precision and recall , the internet , sentiment analysis , particle swarm optimization , machine learning , natural language processing , world wide web , linguistics , philosophy , programming language
With the widespread application of multimedia technology, video barrage has become an important form of expressing opinions and emotions. The classification of Chinese barrage texts according to emotions is of great significance for mining users’ emotions. However, the large number of Internet buzzwords in the barrage texts brings certain difficulties to sentiment analysis. Therefore, in order to address the ambiguity problem of Internet buzzwords in the barrage texts and effectively classifying the video barrage texts, the BERT pre-training language model and Support Vector Machine are combined to classify the barrage texts of positive and negative emotions. In this paper, the word vectors of the barrage texts are firstly generated by the BERT model. The SVM model is used as a classifier, and Particle Swarm Optimization is introduced to optimize the parameters of the SVM model. This method is compared with the traditional methods on the barrage data sets of different video types. The experimental results show that the precision, recall and F1-score of the proposed method are higher than those of the traditional models.