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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | Deep Learning-Based Multi-Class Sentiment Analysis of Social Media Tweets |
| Authors Name: | Priyanshu Sahu , Ashutosh Narayan Sharma , Raj Choubey , Raj Rajeshwar Yadu , Rashmi Banchhor |
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IJRTI_211554
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| Published Paper Id: | IJRTI2604142 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Abstract — The way we communicate has changed a lot because of communication systems and social media platforms like Twitter, Facebook. These platforms generate an amount of user-generated content every day. This content shows what people think and feel about things. To get information from this content is a big challenge. We need to analyze this data to understand what people think about a product or a service. This is important for things like customer feedback analysis, brand monitoring and decision support systems. But the way people write on media is informal they use slang, abbreviations and their grammar is not always correct. This makes it hard to analyze the data. Sentiment analysis is a part of Natural Language Processing (NLP). It is about finding and categorizing opinions, emotions and attitudes in text. We have used machine learning methods like Naïve Bayes, Support Vector Machines (SVM) and Logistic Regression to do sentiment analysis. These methods use feature extraction techniques like Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) to convert text into numbers. These methods are not very good at understanding the context of the text and the relationships between words. Recent advancements in learning, particularly transformer-based architectures have improved the performance of NLP systems. Transformers can process sequences of text at the same time and understand the context. Models like BERT, RoBERTa and DistilBERT have shown results in sentiment analysis tasks. These models create embeddings by looking at the text on both sides of a word. This helps to interpret the text accurately. In this research we propose a sentiment analysis system that uses transformer-based models. The system is a web-based application that can predict sentiment in time. The system has stages, such as data preprocessing, tokenization, embedding generation and model inference. We used a dataset of airline-related tweets to test the system. This dataset is a benchmark because it is informal and noisy. The results show that RoBERTa is more accurate than BERT and DistilBERT.. Distilbert is faster and uses less computational power making it good for real-time applications. BERT is a balance between accuracy and computational complexity. The proposed system is a solution for sentiment analysis in real-world applications. It uses the strengths of transformer-based models to improve the accuracy and reliability of sentiment classification. It can be used in domains, like social media monitoring, customer feedback analysis and decision support systems, where understanding user sentiment is important.. The system can be used to monitor media and understand what people think about a product or a service, It can be used to analyze customer feedback and improve services. It can be used to make decisions based on user sentiment. Overall the system is scalable, efficient and practical. It can be applied in domains where understanding user sentiment is important. |
| Keywords: | Keywords: Sentiment Analysis, Transformer-Based Models, RoBERTa, BERT, DistilBERT, Natural Language Processing (NLP), Deep Learning, Text Classification, Self-Attention Mechanism, Real-Time Prediction |
| Cite Article: | "Deep Learning-Based Multi-Class Sentiment Analysis of Social Media Tweets", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b56-b61, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604142.pdf |
| Downloads: | 00036 |
| 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 |
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Published Paper ID: IJRTI2604142
Registration ID:211554
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b56-b61 Country: Raipur, Chattisgarh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604142 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604142 |
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ISSN: 2456-3315
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