Early Unimodal Sentiment Analysis of Comment Text Based on Traditional Machine Learning

This chapter introduces the main topics and objectives discussed in subsequent sections, covering various aspects of opinion mining and sentiment analysis. It addresses different challenges and proposes novel methods. Section 3.1 highlights the need to filter out irrelevant information and personal attacks in online discussions to focus on evaluative opinion sentences. It proposes an unsupervised method utilizing natural language processing techniques and machine learning algorithms to automatically filter and classify sentences with evaluative opinions. This section calls for further research to explore more precise and efficient methods for identifying evaluative opinions and their application in sentiment polarity analysis. Section 3.3 presents a novel subproblem in opinion mining, focusing on grouping feature expressions in product reviews. It argues for the necessity of user supervision in practical applications and proposes an EM formulation enhanced with soft constraints to achieve accurate opinion summaries. This section showcases the competence and generality of the proposed method through experimental results from various domains and languages. Section 3.1 introduces the use of topic modeling, specifically the LDA method, for sentiment mining. It extends the LDA method to handle large-scale constraints and proposes two methods for automatically extracting constraints to guide the topic modeling process. The constrained-LDA model and extracted constraints are then applied to group product features, demonstrating superior performance compared to other methods. Section 3.3 addresses the challenge of grouping synonyms in opinion mining, proposing an efficient method based on similarity measurement. Experimental results from different domains validate the effectiveness of the method. Section 3.3 focuses on feature extraction for sentiment classification and compares the impact of different types of features through experimental analysis. This section provides an in-depth study of all feature types and discusses key problems associated with feature extraction algorithms. Section 3.2 explores the use of unsupervised learning methods for sentiment classification, emphasizing their advantages in classifying opinionated texts at different levels and for feature-based opinion mining. This section presents an empirical investigation of unsupervised sentiment classification of Chinese reviews and proposes an algorithm to remove domain-specific sentiment noise words. Section 3.6 introduces the use of substring-group features for sentiment classification through a transductive learning-based algorithm. Experimental results in multiple languages demonstrate the effectiveness of the algorithm and highlight the superiority of the “tfidf-c” approach for term weighting. Therefore, this chapter provides a comprehensive overview of various aspects of opinion mining and sentiment analysis, and proposes several innovative methods to address different challenges. From filtering and classifying evaluative opinion sentences to grouping product features, and utilizing topic modeling and similarity measurement for sentiment mining, this chapter covers a wide range of topics and techniques. The practicality and superiority of the proposed methods are demonstrated through empirical experiments. These studies lay a foundation for further advancements in opinion mining and sentiment analysis, and hold significant value in practical applications.