Sentiment Analysis of English Text with Multilevel Features

• Published in: Scientific programming Vol. 2022; pp. 1 - 10
• Main Author: Rao, Li
• Format: Journal Article
• Language: English
• Published: New York Hindawi 28.01.2022; Hindawi Limited
• Subjects: Algorithms; Classification; Cooperation; Data mining; Dictionaries; Emotions; Information retrieval; International relations; Machine learning; Model accuracy; Multilevel; Natural language processing; Semantics; Sentiment analysis; Social networks; Support vector machines; Texts
• ISSN: 1058-9244; 1875-919X
• DOI: 10.1155/2022/7605125

In natural language processing, text sentiment analysis is one of the important branches. It refers to the use of text mining and other technologies to extract attitudes, opinions, and other information from texts containing emotional information for analysis. Traditional sentiment analysis methods can be roughly divided into two categories: one is dictionary-based methods, and the other is machine learning-based methods. The former relies on the quality of the sentiment dictionary, while the latter relies on a large amount of high-quality data, so both have certain limitations. In text sentiment analysis research, word-level and sentence-level sentiment information extraction is a basic research task and has important research value. Through research, it is found that domain knowledge and context are two important factors influencing the extraction of emotional information. To this end, this paper proposes a text sentiment analysis method that integrates multiple features and constructs three features, which are based on the sentiment value feature of the dictionary, the expression feature, and the improved semantic feature, which are combined to build a text sentiment classification model. Aiming at the colloquial, irregular, and diverse features of English social media texts, this paper proposes a multilevel feature representation method. The sentiment classification experiments on English text show that the multilevel features proposed in this paper can effectively improve the F1_macro and accuracy of multiple model classifications. Compared with the existing research, the model in this paper improves the effect the most obvious.