IJNRD Research Journal

WhatsApp
Click Here

WhatsApp editor@ijnrd.org
IJNRD
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.76

Issue per Year : 12

Volume Published : 9

Issue Published : 94

Article Submitted :

Article Published :

Total Authors :

Total Reviewer :

Total Countries :

Indexing Partner

Join RMS/Earn 300

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: SENTIMENT CLASSIFICATION ACROSS MULTIPLE SOURCES USING AN INTEGRATED POLARITY-SCORE BASED EMBEDDING IN DEEP LEARNING MODEL
Authors Name: PARVATI KADLI , Dr. VIDYAVATHI B M
Download E-Certificate: Download
Author Reg. ID:
IJNRD_197515
Published Paper Id: IJNRD2305918
Published In: Volume 8 Issue 5, May-2023
DOI: http://doi.one/10.1729/Journal.34906
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
Keywords: Convolutional neural network; cross-domain data; sentiment analysis; social media; Facebook; Twitter; Amazon; Tripadvisor.
Cite Article: "SENTIMENT CLASSIFICATION ACROSS MULTIPLE SOURCES USING AN INTEGRATED POLARITY-SCORE BASED EMBEDDING IN DEEP LEARNING MODEL", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.j19-j31, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305918.pdf
Downloads: 000118749
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID:IJNRD2305918
Registration ID: 197515
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.34906
Page No: j19-j31
Country: HOSPET, VIJANAGARA DISTRICT, KARNATAKA, INDIA
Research Area: Computer Science & Technology 
Publisher : IJ Publication
Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305918
Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305918
Share Article:
Share

Click Here to Download This Article

Article Preview
Click Here to Download This Article

Major Indexing from www.ijnrd.org
Semantic Scholar Microsaoft Academic ORCID Zenodo
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX PUBLON
DRJI SSRN Scribd DocStoc

ISSN Details

ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)


Providing A digital object identifier by DOI
How to Get DOI? DOI

Conference

Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Social Media

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Join RMS/Earn 300

IJNRD