Cross-collection multi-aspect sentiment analysis

Kaporo, Hemed Hamisi (2019) Cross-collection multi-aspect sentiment analysis. In: 8th Computer Science On-line Conference, CSOC 2019, Prague, Czech Republic

Full text not available from this repository. (Request a copy)


This paper proposes the use of cross-collection topic models to achieve aspect-based sentiment analysis of multiple entities simultaneously. A topic refinement algorithm that enhances semantic interpretability of topics to match that of visually identifiable aspects is presented. It is shown that, with this refinement, topics elicited from cross-collection topic models align excellently with entity aspects. Finally, the utility of opinion words returned from cross-collection topic models in investigated in the task of sentiment analysis. It is concluded that the use of such words as features for sentiment analysis yields more accurate sentiment scores than supervised counterparts.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: Cross collection topic modelling; Multi-entity multi-aspect sentiment analysis; Opinion mining
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Hemed Hamisi Kaporo
Date Deposited: 22 Jul 2023 17:31
Last Modified: 22 Jul 2023 17:31

Actions (login required)

View Item
View Item