Hybrid Learning Approach Based Aspect Category Detection for Sentiment Summarization with Co-Occurrence Data

IJARCSSE

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Field Value
 
Title Hybrid Learning Approach Based Aspect Category Detection for Sentiment Summarization with Co-Occurrence Data
 
Creator Sravani, CH.
Ramu, Y.
 
Description User-generated reviews are precious decision-making resources. Identifying the feature categories mentioned in a specified review phrase (e.g. "food" and "service" in restaurant reviews) is a significant task for analyzing sentiment and mining opinion. Most prior researchers hold hand-crafted characteristics and a classification algorithm to achieve the assignment given a predefined aspect category set. The key step to achieve better efficiency is feature engineering that consumes a great deal of human effort and can be volatile when the product domain changes. A hybrid learning method is suggested in this project to automatically learn helpful characteristics for the identification of aspect categories. Specifically, on a big collection of reviews with noisy labels, a Hybrid Aspect Analysis Algorithm is first suggested to achieve ongoing word depictions. We subsequently suggest generating deeper and hybrid characteristics through the stacked neural networks on the word vectors. Finally, a logistic regression classifier is trained to predict the aspect category with hybrid characteristics. The tests are conducted on a SemEval-2014 benchmark dataset. In this paper we achieves the state of the art results with the F1 score of 90.10% on the dataset. Overall, our approach to representation learning outperforms traditional hand-crafted characteristics and embedding algorithms with current words.
 
Publisher International Journal of Advanced Research in Computer Science and Software Engineering
 
Contributor
 
Date 2019-10-03
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ijarcsse.com/index.php/ijarcsse/article/view/1069
10.23956/ijarcsse.v9i9.1069
 
Source International Journal of Advanced Research in Computer Science and Software Engineering; Vol 9, No 9 (2019): September 2019; 12-16
2277128X
22776451
10.23956/ijarcsse.v9i9
 
Language eng
 
Relation http://ijarcsse.com/index.php/ijarcsse/article/view/1069/623
 
Rights Copyright (c) 2019 International Journal of Advanced Research in Computer Science and Software Engineering
 

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