Application of Latent Dirichlet Allocation (LDA) for clustering financial tweets

Sifi Fatima-Zahrae, Sabbar Wafae, El Mzabi Amal, S. Krit
2021 E3S Web of Conferences  
Sentiment classification is one of the hottest research areas among the Natural Language Processing (NLP) topics. While it aims to detect sentiment polarity and classification of the given opinion, requires a large number of aspect extractions. However, extracting aspect takes human effort and long time. To reduce this, Latent Dirichlet Allocation (LDA) method have come out recently to deal with this issue.In this paper, an efficient preprocessing method for sentiment classification is
more » ... and will be used for analyzing user's comments on Twitter social network. For this purpose, different text preprocessing techniques have been used on the dataset to achieve an acceptable standard text. Latent Dirichlet Allocation has been applied on the obtained data after this fast and accurate preprocessing phase. The implementation of different sentiment analysis methods and the results of these implementations have been compared and evaluated. The experimental results show that the combined uses of the preprocessing method of this paper and Latent Dirichlet Allocation have an acceptable results compared to other basic methods.
doi:10.1051/e3sconf/202129701071 fatcat:e6eerbzhpnfirihqqhtuwdd7jq