A Systematic Review on Features Extraction Techniques for Aspect Based Text Classification using Artificial Intelligence

Title

A Systematic Review on Features Extraction Techniques for Aspect Based Text Classification using Artificial Intelligence

Creator

Nagendra N.; Chandra J.

Description

Aspect Extraction is an important, challenging, and meaningful task in aspect-based text classification analysis. To apply variants of topic models on task, while reasonably successful, these methods usually do not produce highly coherent aspects. This review presents a novel neural/cognitive approach to discover coherent methods. They exploited the distribution of word co-occurrences through neural/cognitive word embeddings. Unlike topics that typically assume independently generated words, word embedding models encourage words that appear in similar factors close to each other in the embedding space. Also, use an attention mechanism to de-emphasize irrelevant words during training, improving aspects coherence. Methods results on datasets demonstrate that the approach discovers more meaningful and coherent aspects and substantially outperforms baseline. Aspect-based text analysis aims to determine people's attitudes towards different aspects in a review. The Electrochemical Society

Source

ECS Transactions, Vol-107, No. 1, pp. 2503-2514.

Date

2022-01-01

Publisher

Institute of Physics

Coverage

Nagendra N., Department of Computer Science, Christ University, Karnataka, Bangalore, 560029, India; Chandra J., Department of Computer Science, Christ University, Karnataka, Bangalore, 560029, India

Rights

Restricted Access

Relation

ISSN: 19386737; ISBN: 978-160768539-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1149/10701.2503ecst

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130545628&doi=10.1149%2f10701.2503ecst&partnerID=40&md5=9ac389b5a788b4b9a27946763972f6cb