Abstract
As a tool to study people’s views and opinions on things and events around them, sentiment analysis is widely used in the analysis and processing of mass evaluation information. Traditional emotion analysis is generally based on emotion dictionary, but the construction of emotion dictionary needs a lot of artificial time, and in different application fields need to establish different emotion dictionaries. Meanwhile, emotion dictionary can’t contain the semantic information of words and it also ignore the role of non-emotional words in the expression of emotion. This paper proposes a new emotion analysis algorithm (CBOW-PE) based on word embedding and part of speech. First, in the stage of pre-processed experiment, we use part of speech to preprocess the experimental data, fully consider the role of non-emotional words on emotion analysis. And then we add emotional information to assist word vector training, so that the word embedding of words related to emotion has both semantic information and emotional information. Finally, this paper makes a lot of experimental analysis and comparison of this paper and its related algorithms. The results show that the algorithm is effective and efficient in Chinese emotion analysis.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call