Sentiment Analysis of English Text with Multilevel Features

Li Rao,Baiyuan Ding

doi:10.1155/2022/7605125

Abstract

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.

Highlights

English is the world’s largest language in Europe, America, Oceania, Asia, and other dozens of countries, and the total number of people who use it as a mother tongue or a second language is about hundreds of millions
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
E following conclusions can be drawn: (1) Compared with the CNN model with only original semantic features, the accuracy of the TCNN model with improved semantic features is increased by 2.19%, indicating that the combination of TFIDF algorithm improves the keyword in the text. e weight is beneficial to the improvement of sentiment classification performance and verifies the effectiveness of the improved semantic features. (2) e ECNN model, which integrates expression features on the basis of original semantic features, has an accuracy rate of 1.98% higher than that of the CNN model, which shows that emojis increase the effect of indicating emotions, and proves the necessity of adding expression features

Summary

Introduction

English is the world’s largest language in Europe, America, Oceania, Asia, and other dozens of countries, and the total number of people who use it as a mother tongue or a second language is about hundreds of millions. Many researchers continue to improve and supplement emotional dictionaries and have made some progress, this method is always limited by the dictionary, it is impossible to include all emotional words, and it cannot adapt to the times Because it is not suitable for texts with implicit sentiment characteristics, the accuracy of this method has not been high when used in text sentiment analysis [16,17,18,19]. For complex English, traditional machine learning modeling methods cannot achieve satisfactory results 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

Text Sentiment Analysis Combining Multiple Features

Result

Experiment and Analysis

Findings

Conclusion