Extraction of Opinion Target Using Syntactic Rules in Urdu Text
Opinion target or aspect extraction is the key task of aspect-based sentiment analysis. This task focuses on the extraction of targeted words or phrases against which a user has expressed his/her opinion. Although, opinion target extraction has been studied extensively in the English language domain...
Gespeichert in:
Veröffentlicht in: | Intelligent automation and soft computing 2021-01, Vol.29 (3), p.839-853 |
---|---|
Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Opinion target or aspect extraction is the key task of aspect-based sentiment analysis. This task focuses on the extraction of targeted words or phrases against which a user has expressed his/her opinion. Although, opinion target extraction has been studied extensively in the English language domain, with notable work in other languages such as Chinese, Arabic etc., other regional languages have been neglected. One of the reasons is the lack of resources and available texts for these languages. Urdu is one, with millions of native and non-native speakers across the globe. In this paper, the Urdu language domain is focused on to identify opinion targets from written Urdu texts. To accomplish this task, several syntactic rules are crafted to identify users' opinions and associated target words. These rules are crafted using the grammatical and linguistic context of the words in the sentence. To the best of our knowledge, there is no existing work available in the Urdu domain for opinion target extraction. The proposed methodology is evaluated on an Urdu language dataset and compared with an existing approach for the English language by applying the same technique. The experiments have demonstrated that the proposed approach achieves promising performance as compared to the applied English language domain approach. |
---|---|
ISSN: | 1079-8587 2326-005X |
DOI: | 10.32604/iasc.2021.018572 |