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Introduction:

Social media has become an integral part of people’s daily lives, providing a platform for individuals to express their opinions, thoughts, and feelings about various topics, including brands. Brands are increasingly interested in understanding the sentiment associated with their mentions on social media, as it can provide valuable insights into consumer preferences, satisfaction, and brand perception. Sentiment analysis, a technique used to determine the sentiment expressed in text data, can be a powerful tool for brands to gauge public opinion and make informed decisions.

This thesis focuses on sentiment analysis of brand mentions on social media using text mining and deep learning techniques. By analyzing the sentiment of brand mentions, brands can gain a deeper understanding of customer sentiment and improve their marketing strategies accordingly. This research aims to explore the potential of text mining and deep learning in sentiment analysis and provide insights into how brands can leverage these techniques effectively.

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 techniques
2.4 Sentiment analysis on social media
2.5 Brand sentiment analysis
2.6 Applications of sentiment analysis in marketing
2.7 Challenges in sentiment analysis
2.8 Previous studies on brand sentiment analysis
2.9 Current trends in sentiment analysis
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Introduction
3.2 Data collection
3.3 Data preprocessing
3.4 Text mining process
3.5 Deep learning models
3.6 Sentiment analysis algorithms
3.7 Evaluation metrics
3.8 Experimental design
3.9 Data analysis techniques

Chapter 4: Discussion of Findings
4.1 Introduction
4.2 Analysis of sentiment in brand mentions
4.3 Comparison of text mining and deep learning techniques
4.4 Insights for brand marketing strategies
4.5 Implications for brand reputation management
4.6 Limitations of the study
4.7 Future research directions

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Practical implications
5.4 Recommendations for brands
5.5 Conclusion

Thesis Overview:

Social media has revolutionized the way people communicate and interact with each other, as well as how businesses engage with their customers. With the vast amount of data generated on social media platforms, brands have a unique opportunity to gain valuable insights into consumer sentiment and preferences. Sentiment analysis, a powerful tool for understanding and analyzing text data, can help brands extract meaningful insights from brand mentions on social media.

This thesis focuses on sentiment analysis of brand mentions on social media using text mining and deep learning techniques. By analyzing the sentiment expressed in brand mentions, brands can better understand customer perceptions, identify areas for improvement, and enhance their marketing strategies. The research aims to explore the effectiveness of text mining and deep learning in sentiment analysis and provide practical insights for brands looking to leverage these techniques.

Through a comprehensive literature review, the thesis will examine the current state of sentiment analysis, text mining techniques, deep learning models, and applications of sentiment analysis in marketing. The research methodology will detail the data collection process, data preprocessing, text mining techniques, deep learning models, sentiment analysis algorithms, and evaluation metrics used in the study. The discussion of findings will analyze the sentiment in brand mentions, compare text mining and deep learning techniques, and provide insights for brand marketing strategies.

Overall, this thesis aims to contribute to the existing body of knowledge on sentiment analysis of brand mentions on social media and provide actionable recommendations for brands to improve their marketing efforts. By leveraging text mining and deep learning techniques, brands can gain a competitive edge in understanding customer sentiment and enhancing brand reputation on social media platforms.

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