Sentiment recognition model of Weibo comments based on SVM and Word2vec
As a highly interactive social media platform,Weibo is rich in a large amount of subjective text data.In order to mine the potential information value in the review text,aiming at the problems of semantic loss and excessive dependence on back-ground knowledge in the traditional methods,an emotion recognition model based on SVM and Word2vec is proposed.Through the Skip-gram method in the Word2vec model,the context structure is predicted by using the central word of the current context,and the words are mapped into word vectors,which are transformed into vector matrices and then input into the SVM model for training and classification.The experimental results show that the accuracy of the model is 0.943,the recall rate is 0.941,and the F1-score is 0.946,which contains good generalization.
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