[ad_1]

Introduction:

The rise of social media platforms has revolutionized the way in which people engage with politics, with Twitter being a popular platform for political discourse. As a result, there is a vast amount of political data available in the form of tweets, making it crucial to analyze this data to understand public sentiment towards political issues and figures. Sentiment analysis, a subfield of natural language processing, offers a way to automatically identify and extract sentiment from text data. In recent years, the combination of text mining and deep learning techniques has shown promising results in sentiment analysis tasks.

This thesis aims to explore the use of text mining and deep learning for sentiment analysis of political tweets. By analyzing the sentiment of political tweets, we can gain valuable insights into public opinion towards political events, policies, and figures. The findings of this study can be applied to various domains, including political campaign strategies, public opinion polling, and policy-making.

Table of Contents:

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Introduction to Sentiment Analysis
2.2 Text Mining Techniques
2.3 Deep Learning for Sentiment Analysis
2.4 Sentiment Analysis of Political Tweets
2.5 Applications of Sentiment Analysis in Politics
2.6 Challenges in Sentiment Analysis of Political Tweets
2.7 Previous Studies on Sentiment Analysis of Political Tweets
2.8 Limitations of Existing Approaches
2.9 Research Gaps
2.10 Theoretical Framework

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Preprocessing of Data
3.4 Feature Extraction
3.5 Model Selection
3.6 Evaluation Metrics
3.7 Ethical Considerations
3.8 Data Analysis Techniques

Chapter 4: Findings and Discussion
4.1 Overview of Data
4.2 Results of Sentiment Analysis
4.3 Comparison of Different Models
4.4 Interpretation of Results
4.5 Implications of Findings
4.6 Discussion of Findings
4.7 Comparison with Previous Studies
4.8 Recommendations for Future Research

Chapter 5: Conclusion
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Practice
5.4 Limitations of the Study
5.5 Areas for Future Research
5.6 Conclusion

Thesis Overview:

The Sentiment analysis of political tweets using text mining and deep learning thesis aims to explore the application of text mining and deep learning techniques for sentiment analysis of political tweets. The introduction provides an overview of the study, outlining the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The literature review examines existing knowledge in the field of sentiment analysis, text mining, deep learning, and political sentiment analysis. The research methodology details the research design, data collection, preprocessing, feature extraction, model selection, evaluation metrics, and ethical considerations. The findings and discussion chapter presents the results of the sentiment analysis, interprets the findings, discusses implications, and provides recommendations for future research. The conclusion summarizes the findings, highlights contributions, discusses limitations, and suggests areas for future research.

[ad_2]

---

Purchase Detail

Download the complete project materials to this project with Abstract, Chapters 1 – 5, References and Appendix (Questionaire, Charts, etc), Click Here to place an order via whatsapp. Got question or enquiry; Click here to chat us up via Whatsapp.
You can also call 08111770269 or +2348059541956 to place an order or use the whatsapp button below to chat us up.
Bank details are stated below.

Bank: UBA
Account No: 1021412898
Account Name: Starnet Innovations Limited

The Blazingprojects Mobile App

Download and install the Blazingprojects Mobile App from Google Play to enjoy over 50,000 project topics and materials from 73 departments, completely offline (no internet needed) with monthly update to topics, click here to install.