Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (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)
The goal of social media sentiment analysis is to better understand what people think and feel through digital (Internet) channels. Many different methods are available for conducting sentiment analysis in the social media space, including Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques. When researching the NLP/ML/DL methods that have been used for conducting sentiment analysis on social media data, as well as various forms of visualization (e.g., dashboards, clouds of words, trend analysis, etc.) that serve to improve users' interpretations of the shape and direction of social media sentiment patterns, it is critical to be aware of the current limitations surrounding the methodology and process of conducting sentiment analysis and how the use of visualization can help decision-makers utilize data-driven strategies and make more effective and informed decisions.
Keywords:
Cite Article:
"SOCIAL MEDIA SENTIMENT ANALYSIS AND VISUALIZATION", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.b37-b42, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605107.pdf
Downloads:
000116
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator