Research on sentiment analysis methods for text-oriented data
DOI:
https://doi.org/10.54097/fcis.v3i1.6022Keywords:
Sentiment analysis, Aspect level, Knowledge graph, Pre-training modelAbstract
With the rapid development of information technology, the results obtained from sentiment analysis on a large number of speech information on these platforms can be used for comment classification, product analysis and recommendation, consumption forecast and other aspects of the network platform With the rapid development of information technology, the results obtained from sentiment analysis on a large number of speech information on these platforms can be used for comment classification, product analysis and recommendation, consumption forecast and other aspects of the network platform Sentiment analysis is a practical technique which has become one of the most active research fields in natural language processing. The traditional text sentiment analysis method consumes a lot of human resources, but the coverage of artificial extracted features is the traditional text sentiment analysis method consumes a lot of human resources, but the coverage of artificial extracted features is limited and the artificial irrational behavior will affect the correctness of the results, so the traditional method is not universal. With the development of deep learning, text pre-training language model and knowledge graph technology continue to develop. Aiming at the research of sentiment analysis methods for text data, we summarize the research background and domestic and foreign research status of sentiment analysis methods for text data, and explore the hot research content, key problems, commonly used experimental methods and technical lines of sentiment analysis methods for text text data.
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