Abstract
The technology of the Internet develops rapidly recent years, the public tends to share their reviews, opinions and ideas on the Internet. The forms of these subjective texts are free and concise, and they contain a wealth of sentiment information. In this paper, a modified latent Dirichlet allocation (LDA) model and support vector machine (SVM) are used for sentiment analysis of subjective texts. Analysis of sentiment could help producer to enhance the products and guide user make better choices as well. We apply a modified LDA model using term frequency-inverse document frequency (TF-IDF) algorithm to mine potential topics, find the most relevant words of the topic and represent the document. Then we use SVM to categorize the texts into two classes: positive and negative. Experiment results show that the performance of the modified LDA approach is better than the traditional LDA model.
Project 61471066 supported by NSFC.
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Ye, J., Jing, X., Li, J. (2018). Sentiment Analysis Using Modified LDA. In: Sun, S., Chen, N., Tian, T. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2017. Lecture Notes in Electrical Engineering, vol 473. Springer, Singapore. https://doi.org/10.1007/978-981-10-7521-6_25
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DOI: https://doi.org/10.1007/978-981-10-7521-6_25
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