Aspect-Based Sentiment Analysis for Hotel Reviews Using a Data Augmentation Approach

Yojitha Chilukuri¹ , Ulligaddala Srinivasarao²

Section:Research Paper, Product Type: Journal-Paper
Vol.12 , Issue.5 , pp.1-9, Oct-2024

Online published on Oct 31, 2024

Copyright © Yojitha Chilukuri, Ulligaddala Srinivasarao . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The amount of user-generated textual material created by social networks, blogs, forums, and e-commerce websites is increasing at an astronomical pace. When it comes to determining the success of a product or service, the opinions of customers are critical. Due to this, interest in FE exams and assessment mining has surged. Angle`s put-together feeling examination depends on extracting item qualities from client assessments utilizing subject demonstrating and Latent Dirichlet Allocation (LDA). Because of information sparsity and the non-appearance of co-event designs in short texts, LDA won`t be quickly applied to client audits and other short texts. Various methods have been distributed for adapting the latest models like LDA for short. A Pachinko Allocation Model (PAM) is proposed in this paper as a one-of-a-kind methodology for opinion examination because of perspectives. The Pachinko Allocation Model is a new PAM adaptation that extracts product aspects. Data augmentation increases the text data set size for the text classification task. After that, features are extracted using TF-IDF-IC-SDF and TF-IGM methods, and the fine sentiment is extracted utilizing the opinion lexicon. According to the findings of the experiments, PAM is a competitive method for extracting aspects. The outcomes of the trial show that the novel sentiment classification approach is competitive in terms of product extraction. A statistical test has also been conducted.

Key-Words / Index Term :
Feature Extraction, PAM, Aspect-based Sentiment Analysis, Topic Modelling

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