A Review of Text Sentiment Analysis Methods and Applications
DOI:
https://doi.org/10.54097/fbem.v10i1.10171Keywords:
Natural Language Processing, Sentiment Analysis, Machine Learning, Deep Learning.Abstract
This paper reviews the application of natural language processing in sentiment analysis. Sentiment analysis is an important task aimed at automatically identifying and inferring sentiment tendencies and sentiment intensity in texts. This paper first introduces the application areas of sentiment analysis, including practical applications of text sentiment analysis. Then, text pre-processing techniques such as word separation, deactivation removal and punctuation processing are discussed. Then, feature extraction and representation methods are explored, including bag-of-words model, TF-IDF, word embedding and Word2Vec, attention mechanism and Transformer. In addition, methods for sentiment analysis, such as sentiment dictionaries and rule-based methods, traditional machine learning methods, and deep learning-based methods, are presented. Finally, the application areas of sentiment analysis are discussed and conclusions are given. The review in this paper will help readers understand the current status and development trend of natural language processing applications in sentiment analysis, as well as the advantages and disadvantages of different methods in sentiment analysis.
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