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Introduction

In recent years, social media has become an essential platform for individuals and organizations to express their opinions, share information, and engage with their audience. With the increasing popularity of social media, it has become crucial for brands to monitor and analyze the sentiment of social media posts to manage their reputation effectively. Sentiment analysis, also known as opinion mining, is a technique used to determine the attitude or emotion expressed in a piece of text. By analyzing the sentiment of social media posts, brands can gain valuable insights into how their products or services are perceived by their customers and make informed decisions to improve customer satisfaction and brand reputation.

This thesis focuses on the use of text mining and deep learning techniques for sentiment analysis of social media posts for brand reputation monitoring. Text mining involves extracting useful information and knowledge from unstructured text data, while deep learning is a subset of machine learning that uses neural networks to learn complex patterns in data. By leveraging these advanced technologies, we aim to develop a robust sentiment analysis model that can accurately classify social media posts into positive, negative, or neutral sentiments.

Table of Content

1.1 Introduction
1.2 Background of the 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 Social Media Sentiment Analysis
2.2 Text Mining Techniques
2.3 Deep Learning for Sentiment Analysis
2.4 Brand Reputation Monitoring
2.5 Text Classification
2.6 Natural Language Processing
2.7 Neural Networks
2.8 Feature Extraction
2.9 Sentiment Analysis Tools
2.10 Existing Sentiment Analysis Studies

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Feature Extraction
3.4 Model Development
3.5 Model Evaluation
3.6 Performance Metrics
3.7 Experimental Design
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Performance Comparison
4.2 Feature Importance Analysis
4.3 Error Analysis
4.4 Model Interpretability
4.5 Implications for Brand Reputation Management

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Research Directions
5.4 Practical Implications
5.5 Conclusion

Thesis Overview

The emergence of social media has revolutionized the way consumers communicate and interact with brands. As a result, brands have realized the importance of monitoring social media posts to understand customer sentiments and manage their reputation effectively. Sentiment analysis, which involves analyzing and classifying opinions expressed in text data, has become a valuable tool for brands to gain insights into customer perceptions and make data-driven decisions.

This thesis focuses on the application of text mining and deep learning techniques for sentiment analysis of social media posts to monitor brand reputation. The study aims to develop a robust sentiment analysis model that can accurately classify social media posts into positive, negative, or neutral sentiments. By leveraging advanced technologies such as neural networks, the proposed model seeks to improve the accuracy and efficiency of sentiment analysis for brand reputation monitoring.

By conducting a comprehensive literature review, implementing a rigorous research methodology, and analyzing the findings, this thesis aims to provide valuable insights into the effectiveness of sentiment analysis for brand reputation monitoring. The results of this study are expected to contribute to the existing body of knowledge on sentiment analysis and inform practical implications for brand managers and marketers.

Overall, this thesis seeks to advance the field of sentiment analysis and demonstrate the significance of leveraging text mining and deep learning techniques for monitoring brand reputation in the era of social media.

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