Research for the methods of text sentiment analysis

Sentiment analysis, as a critical technology for understanding and interpreting massive textual information, has been widely applied in public opinion monitoring and decision support for enterprises and governments. However, existing sentiment analysis methods still face challenges in handling complex semantics and contextual relationships. This paper provides a synthesis of the main current text sentiment analysis methods, which include approaches based on sentiment lexicons, machine learning, and recently developed deep learning techniques. Firstly, this paper introduces the construction of sentiment lexicons and their application in sentiment analysis, exploring the impact of different lexicons on analysis results. Next, it discusses machine learning-based methods, detailing text preprocessing, tokenization techniques, and the training and evaluation of sentiment classification models. Lastly, it analyzes deep learning-based methods, highlighting their advantages in automatic feature extraction and handling complex semantics. By comparing the advantages and disadvantages of various methods, it is found that sentiment lexicon-based methods excel in computational efficiency and interpretability but have limitations in dealing with complex semantics. Machine learning-based methods better capture features within the text but require large datasets and fine-tuned parameters. Deep learning-based methods perform excellently in processing complex sentiments and contextual relationships but demand significant computational resources and data volume. This paper proposes a hybrid model combining sentiment lexicons, machine learning, and deep learning techniques as a future research direction to improve the precision and speed of sentiment analysis.