A Systematic Review on Features Extraction Techniques for Aspect Based Text Classification using Artificial Intelligence
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
ECS Transactions, Vol-107, No. 1, pp. 2503-2514.
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
Nagendra N., Department of Computer Science, Christ University, Karnataka, Bangalore, 560029, India; Chandra J., Department of Computer Science, Christ University, Karnataka, Bangalore, 560029, India