Sentiment Analysis Final Year Projects with Source Code

Byadmin Final Year Projects

Sentiment Analysis Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Sentiment Analysis projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Sentiment Analysis Final Year Projects

  1. A Machine Learning-Sentiment Analysis on Monkeypox Outbreak An Extensive Dataset to Show the Polarity of Public Opinion From Twitter Tweets
    This project studies public reactions to the recent monkeypox outbreak by analyzing social media posts. Researchers collected over 500,000 tweets and labeled them as positive, negative, or neutral. They tested many machine learning models to find the best way to predict public sentiment. The study found that a model using TextBlob, lemmatization, CountVectorizer, and SVM gave the most accurate results, helping health authorities understand public concerns.
  2. A Multi-Stage Machine Learning and Fuzzy Approach to Cyber-Hate Detection
    This project focuses on detecting harmful content on social media. It uses machine learning methods to classify online messages as hateful or not. The study tests two models on multiple datasets and improves their accuracy using nature-inspired optimization techniques and fuzzy logic. This approach helps the system better understand the meaning behind the text.
  3. A Text Mining and Statistical Approach for Assessment of Pedagogical Impact of Students Evaluation of Teaching and Learning Outcome in Education
    This study looks at how technology is used to support teaching and learning in universities. It analyzed almost half a million student evaluations to understand what affects students’ performance and their experience of digital education. The research combined text analysis and statistical methods to study students’ feedback and emotions over time. The findings show that flexible digital teaching methods work well, but more research is needed to see how they impact learning outcomes.
  4. Boosted Barnacles Algorithm Optimizer Comprehensive Analysis for Social IoT Applications
    This project focuses on improving the Social Internet of Things (SIoT), where smart devices share data for health monitoring, emergency alerts, and learning systems. It introduces a new method using the Barnacles Mating Optimizer to make data transfer faster and more accurate. The method was tested on real datasets and showed better performance than existing approaches. Overall, it helps smart devices work together more efficiently.
  5. An Adaptive Masked Attention Mechanism to Act on the Local Text in a Global Context for Aspect-Based Sentiment Analysis
    This project studies how to understand opinions about specific parts of a sentence, such as features of a product. It introduces a new way for the model to focus on both the whole sentence and the important local words. This method reduces noise and helps the system learn useful information more efficiently. The model works well on many benchmark datasets.
  6. An Enhanced Recommendation Model Based on Review Text Graph and Interaction Graph
    This project improves how online recommendation systems understand users. It uses both the text of user reviews and user ratings to learn what people like. The model studies the full structure of review sentences, not just nearby words. It then combines this with rating patterns to give more accurate recommendations.
  7. Data Analysis in Social Networks for Agribusiness A Systematic Review
    This project studies how social networks like Twitter can help improve decision-making in agriculture. It looks at how companies can use online data to generate knowledge and respond to market changes. The researchers reviewed over 200 studies to see how social media supports agribusiness. They found that social media monitoring can complement traditional methods but more research is needed.
  8. Deep Learning Using Context Vectors to Identify Implicit Aspects
    This project focuses on finding the hidden topics that people talk about in their reviews. It looks for meanings that are not directly written but are implied through the words people use. The system learns from examples and understands the surrounding text to detect these hidden ideas. It helps improve sentiment analysis by making it more accurate and closer to real human understanding.
  9. Recommendation System Based on Deep Sentiment Analysis and Matrix Factorization
    This project develops a smarter recommendation system for online platforms. It analyzes user reviews to understand preferences and feelings. Then it combines this information with ratings to predict what users will like. Tests on Amazon data show it works better than traditional methods.
  10. Applications of Artificial Intelligence in the Economy Including Applications in Stock Trading Market Analysis and Risk Management
    This project studies how Artificial Intelligence (AI) can be used in economics. It looks at applications like stock trading, market analysis, and assessing financial risks. The research organizes different AI methods and explains how they are evaluated. It also highlights current challenges and suggests directions for future work.
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Sentiment Analysis Project Synopsis & Presentation

Final Year Projects helps prepare Sentiment Analysis project synopsis, including problem statement, objectives, existing system, disadvantages, proposed system, advantages and research motivation. We provide PPT slides, tutorials, and full documentation for presentations.

Sentiment Analysis Project Thesis Writing

Final Year Projects provides thesis writing services for Sentiment Analysis projects. We help BE, BTech, ME, MSc, MCA and MTech students complete their final year project work efficiently.

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