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WEAKLY-SUPERVISED DEEP EMBEDDING FOR CUSTOMER FEEDBACK SENTIMENT ANALYSIS
Authors
PRADEEPTHI GAJULA,N .NAVEEN KUMAR
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Keywords
Deep learning Weak-Supervision, Opinion Mining, Classification, Sentiment Analysis
Abstract
The E-commerce business levels are every day rising, people are getting adopted to using on the web and composing remarks about their buy encounters on seller/audit Websites. These obstinate substances are gainful to both future purchasers for essential initiative and to sellers to enhance their products and moreover organization. To lighten this issue, numerous data mining approaches are designed which uses human efforts. Today, deep learning has risen as a powerful method for classification problems. We propound new deep learning system WDE that could use the huge amount of pitifully sorted audit reviews for the sentiment analysis. WDE attempts to catch the assessment circulation of the information by inserting preparing on pitifully marked sentences. At that point, it utilizes a couple of marked sentences for profound system adjusting just as for forecast model learning. In this system first, we train the data by CNN-WDE Classifier and then view the features vector. After that Detect the Sentimental labels. Then perform the classification and we can get the exact positive or negative view in the Sentiment Prediction Graph. To assess the proposed framework, we took a dataset containing unlabeled reviews from Amazon.
IJCRT's Publication Details
Unique Identification Number - IJCRT2008265
Paper ID - 197961
Author type - Indian Author
Page Number(s) - 2413-2423
Pubished in - Volume 8 | Issue 8 | August 2020
DOI (Digital Object Identifier) -
No Of Downloads - 1315
Author Country - 500013, INDIA, hyderabad, hyderabad, INDIA, Science & Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
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PRADEEPTHI GAJULA,N .NAVEEN KUMAR, "WEAKLY-SUPERVISED DEEP EMBEDDING FOR CUSTOMER FEEDBACK SENTIMENT ANALYSIS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 8, pp.2413-2423, August 2020, Available at :http://www.ijcrt.org/papers/IJCRT2008265.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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