Hybrid Models for Aspects Extraction without Labelled Dataset


Khong, Wai Howe and Soon, Lay Ki and Goh, Hui Ngo (2019) Hybrid Models for Aspects Extraction without Labelled Dataset. In: The Second Workshop on Fact Extraction and Verification (FEVER 2019), 3-7 Nov. 2019, Hong Kong.

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One of the important tasks in opinion mining is to extract aspects of the opinion target. Aspects are features or characteristics of the opinion target that are being reviewed, which can be categorised into explicit and implicit aspects. Extracting aspects from opinions is essential in order to ensure accurate information about certain attributes of an opinion target is retrieved. For instance, a professional camera receives a positive feedback in terms of its functionalities in a review, but its overly high price receives negative feedback. Most of the existing solutions focus on explicit aspects. However, sentences in reviews normally do not state the aspects explicitly. In this research, two hybrid models are proposed to identify and extract both explicit and implicit aspects, namely TDM-DC and TDM-TED. The proposed models combine topic modelling and dictionary-based approach. The models are unsupervised as they do not require any labelled dataset. The experimental results show that TDM-DC achieves F1-measure of 58.70%, where it outperforms both the baseline topic model and dictionarybased approach. In comparison to other existing unsupervised techniques, the proposed models are able to achieve higher F1-measure by approximately 3%. Although the supervised techniques perform slightly better, the proposed models are domain-independent, and hence more versatile

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Data sets
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 14 Oct 2021 14:11
Last Modified: 14 Oct 2021 14:11
URII: http://shdl.mmu.edu.my/id/eprint/9539


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