2020
DOI: 10.22266/ijies2020.0229.18
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A Filter Based Improved Decision Tree Sentiment Classification Model for RealTime Amazon Product Review Data

Abstract: E-Commerce product features and reviews are considered to be the essential factors in real-time e-commerce sites for product recommendation systems. Due to inaccuracy decision patterns, in most cases e-commerce user fails to predict the products based on the user ratings and review comments. Traditional sentiment classification models are independent of data filtering, transformation and sentiment score computing techniques which require high computing memory, time and mostly leading to false-positive rate. To… Show more

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Cited by 20 publications
(12 citation statements)
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“…Feature level fusion was applied by proposing a NWAE mechanism as a feature selection measure to overcome the problem of dimensionality. The obtained results obtained from speech and three variants of text analysis models are compared with the individual text feature extraction techniques [18] [19] [20] and proved that the proposed hybrid level fusion mechanism improves the user readability by faster access and there by improves the performance.…”
Section: Discussionmentioning
confidence: 99%
“…Feature level fusion was applied by proposing a NWAE mechanism as a feature selection measure to overcome the problem of dimensionality. The obtained results obtained from speech and three variants of text analysis models are compared with the individual text feature extraction techniques [18] [19] [20] and proved that the proposed hybrid level fusion mechanism improves the user readability by faster access and there by improves the performance.…”
Section: Discussionmentioning
confidence: 99%
“…A Filter-Based Improved Decision Tree Sentiment Classification Model for Real-Time Amazon Product Review Data was developed by Syamala et al 38 F-measure, error rate, gain ratio, and kappa statistics were the performance metrics employed for evaluation. The results obtained by this approach have enhanced classification accuracy but this approach failed to filter the fake news.…”
Section: Review Of Related Workmentioning
confidence: 99%
“…The values acquired from the DT are simple and easy to prepare. The small alteration in the system can make a massive impact on the optimized decision tree [25]. Naïve Bayes (NB) [26] is a learning technique for multi-domain and large-scale information in the healthcare domain, which is utilized in cancer prediction.…”
Section: B Feature Selection and Classification Approachesmentioning
confidence: 99%