E3S Web Conf.
Volume 297, 2021The 4th International Conference of Computer Science and Renewable Energies (ICCSRE'2021)
|Number of page(s)||6|
|Published online||22 September 2021|
Application of Latent Dirichlet Allocation (LDA) for clustering financial tweets
1 Laboratoire Informatique de Mohammedia, Faculté des Sciences et Techniques Mohammedia, Maroc
2 Laboratoire Performance Economique et Logistique, Faculté des Sciences Juridiques, Economiques et Sociales Mohammedia, Maroc
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 presented 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.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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