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

Abstract

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 text with fuzzy sentiment boundaries. Taking the review text 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. Based on TF-IDF and LDA to extract and expand text features, the GBDT text sentiment classifier with the accuracy of 0.881; the F1-measure of the second, third, fourth, and fifth categories samples are 0.462, 0.571, 0.278, and 0.647 respectively, which is better than SVM, RF, and XGBoost classifiers and has the best classification performance.

Keywords

• Sentiment analysis
• text classification
• LDA topic model
• crowdsourcing participants