Title: A construction and self-learning method for intelligent domain sentiment lexicon

Authors: Shaochun Wu; Qifeng Xiao; Ming Gao; Guobing Zou

Addresses: Department of Intelligent Information Processing, Shanghai University, Shanghai 200444, China ' Department of Intelligent Information Processing, Shanghai University, Shanghai 200444, China ' Department of Intelligent Information Processing, Shanghai University, Shanghai 200444, China ' Department of Intelligent Information Processing, Shanghai University, Shanghai 200444, China

Abstract: A new method of building intelligent sentiment lexicon based on LDA and word clustering is put forward in this paper. In order to make seed words more representative and universal, this method uses LDA topic model to build the term vectors and select seed words. The improved SO-PMI algorithm has been used to calculate the emotional tendency of each sentiment word. In addition, the domain sentiment lexicon's automatic extension and update method is designed to deal with dynamic corpus data. Experiments show that the proposed method can build the sentiment lexicon with higher accuracy, and can reflect the change of words' emotional tendency in real time. It is proved in this paper that this method is more suitable for processing a large number of dynamic Chinese texts.

Keywords: sentiment lexicon; SO-PMI algorithm; seed words; LDA topic model; word clustering; incremental text processing.

DOI: 10.1504/IJITM.2020.10028762

International Journal of Information Technology and Management, 2020 Vol.19 No.4, pp.318 - 333

Received: 17 Jul 2018
Accepted: 26 Mar 2019

Published online: 22 Apr 2020 *

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