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

Shaochun Wu, Qifeng Xiao, Ming Gao and Guobing Zou

International Journal of Information Technology and Management, 2020, vol. 19, issue 4, 318-333

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. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=110235 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijitma:v:19:y:2020:i:4:p:318-333

Access Statistics for this article

More articles in International Journal of Information Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().