Sentiment Analysis of Social Media Data: Understanding Public Perception
Clicks: 1
ID: 312948
2024
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #456 of 705 articles by views in Journal of Computing & Biomedical Informatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
This paper provides a sentiment analysis model that combines dimensionality reduction, part-of-speech tagging, and natural language processing (NLP) for social media data. The model uses machine learning methods (Naive Bayes, Support Vector Machine, and K-Nearest Neighbor) to categorize sentiment as positive, negative, or neutral properly. The model's performance was assessed using two datasets and compared to other sentiment analysis algorithms that were already in use. The outcomes show increased performance and offer perceptions of the public's thoughts on various topics. This work addresses the problem of language-specific models and advances the creation of accurate sentiment analysis models. In contrast to conventional polls, the study's conclusions present a novel viewpoint on public opinion and offer suggestions for improving the platform so that users can access additional options and conveniences. The proposed model has potential applications in social media monitoring, market research, and political analysis. Future work can extend the model to accommodate multiple languages and explore the use of deep learning techniques. By providing a more accurate and efficient sentiment analysis tool, this research contributes to the growing field of social media analytic and its practical applications.
| Reference Key |
imported_1777058002_69ebc0d2616dd
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | M Waleed Arif |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2024 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.