Sentiment Classification of Crowdsourcing Participants' Reviews Text Based on LDA Topic Model

• Published in: IEEE access Vol. 9; p. 1
• Main Authors: Huang, Yanrong, Wang, Rui, Huang, Bin, Wei, Bo, Zheng, Shu Li, Chen, Min
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Boundaries; Classification; Classification algorithms; Classifiers; Crowdsourcing; crowdsourcing participants; Decision trees; Dictionaries; Dirichlet problem; Emotions; Feature extraction; LDA topic model; Sentiment analysis; Support vector machines; Text categorization; text classification
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3101565

The review text received by crowdsourcing participants contains valuable knowledge, opinions, and preferences, which is an important basis for employers to make trading decisions, and crowdsourcing participants to improve service level and quality. However, there are two kinds of emotional polarity in the review text, the attention paid to sentiment classification of review text with fuzzy emotional boundaries is insufficient. This paper proposes a supervised text sentiment classification method with Latent Dirichlet Allocation (LDA) to improve the classification performance of review texts with fuzzy sentiment boundaries. Taking the text reviews of crowdsourcing participants on the Zhubajie platform as the data set, using N-gram, Word2vec, and TF-IDF algorithms to extract text features. The LDA topic model is applied to expand the number of text features and extract eight topics that affect employers' sentiment tendencies. Text classifiers are constructed based on Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GDBT), and Extreme Gradient Boosting (XGBoost) algorithms, and the effectiveness of the sentiment classification methods are verified by ten-fold cross-validation and confusion matrix. Experimental results show that using the LDA topic model to extend the features of review text can effectively alleviate the problem that the classifier is difficult to distinguish the sentiment categories of different emotion polarity words coexisting text, and enhance the ability of emotion boundary fuzzy text classification. The GBDT text sentiment classifier based on TF-IDF and LDA has the best performance in accuracy, recall, and F1-measure.