Paper Title
SENTIMENT CLASSIFICATION ACROSS MULTIPLE SOURCES USING AN INTEGRATED POLARITY-SCORE BASED EMBEDDING IN DEEP LEARNING MODEL
Article Identifiers
Registration ID: IJNRD_197515
Published ID: IJNRD2305918
DOI: http://doi.one/10.1729/Journal.34906
Authors
PARVATI KADLI , Dr. VIDYAVATHI B M
Keywords
Convolutional neural network; cross-domain data; sentiment analysis; social media; Facebook; Twitter; Amazon; Tripadvisor.
Abstract
The increasing prevalence of the Internet and the advent of Web 2.0 have led to a heightened focus on sentiment analysis of freely expressed opinions in different social media platforms. Sentiment analysis plays significant role in various applications such as review-based product recommendations and opinion mining. This study presents cross-domain-labeled Web sources (Amazon and Tripadvisor) in a unique cross-source cross-domain sentiment categorization approach. We propose a novel architecture named the Deep Learned Model with Integrated binary embedding. This model combines the strengths of Bidirectional Long-Short Term Memory (Bi-LSTM), Bi-Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN).. The proposed approach achieves an accuracy of over 80% in sentiment analysis of Facebook and Twitter datasets
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How To Cite (APA)
PARVATI KADLI & Dr. VIDYAVATHI B M (May-2023). SENTIMENT CLASSIFICATION ACROSS MULTIPLE SOURCES USING AN INTEGRATED POLARITY-SCORE BASED EMBEDDING IN DEEP LEARNING MODEL. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), j19-j31. http://doi.one/10.1729/Journal.34906
Issue
Volume 8 Issue 5, May-2023
Pages : j19-j31
Other Publication Details
Paper Reg. ID: IJNRD_197515
Published Paper Id: IJNRD2305918
Downloads: 000121997
Research Area: Computer Science & TechnologyÂ
Country: HOSPET, VIJANAGARA DISTRICT, KARNATAKA, INDIA
Published Paper PDF: https://ijnrd.org/papers/IJNRD2305918.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305918
Crossref DOI: http://doi.one/10.1729/Journal.34906
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition


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