International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Sentiment Analysis for Social Media Post |
|---|---|
| ABSTRACT | In this paper, we introduce a real-time sentiment analysis framework of social media using machine learning. First, we retrieve data from social media sites (for instance, Twitter, Reddit, Instagram) using their APIs, followed by manual or semi-automatic annotation of postings as positive, negative, or neutral. Text is then pretreated (tokenization, removal of punctuation, removal of stop words, et cetera) to transform them into feature vectors (TF-IDF, word embeddings). Multiple models (conventional models: Logistic Regression, SVM(support vector machine), Random Forest, & Deep models: LSTM(long short-term machine & Transformer-based models) are trained & compared. A real-time system is also crafted by utilizing Python APIs. It has been observed that SVM performed up to 89.5% of accuracy, which is better than Naïve Bayes & Logistic Regression, whereas an LSTM-based model would attain an accuracy of about 91%. The tool can trace the sentiment trend in real-time, which is very helpful. In the future, the research will continue to work on the transformer model and the optimized approach of deployment. |
| AUTHOR | SWAYAM PAL, HARSHIT NAMDEV, HARSHAD NAMDEV, PROF.AKANSHA MESHRAM Student, Department of CSE-AIML, Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India Professor, Department of CSE-AIML, Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312144 |
| pdf/144_Sentiment Analysis for Social Media Post.pdf | |
| KEYWORDS | |
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