Emotions are not only the foundation of human life, but also influence all decisions in modern markets. Grasping the reasoning behind our choices is a key element in econom-ic sciences. Sentiment analysis, a tool to extract emotions from text, is used in this thesis to analyze customers’ opinions in various markets. The calculations are done on a server architecture that is designed to be scalable for massive input directly from social net-works. It computes the sentiment score in a flexible multi-stage process and provides several methods of accessing the results.
Subsequently, it is demonstrated how to use the system’s capabilities by implementing various commercial use cases. This includes geographical and demographic analysis. Additionally, the system is able to provide near-real-time results.
Lastly, the thesis concludes by performing several correlation analyses on the collected data. This illustrates how the intensity of emotions vary by the maturity and form of the economic market and affects the participating companies in these markets.
Inhaltsverzeichnis (Table of Contents)
- 1. Introduction
- 1.1. Goal of the Thesis
- 1.2. Chapter Outline
- 2. Sentiment Analysis
- 2.1. Goals of Sentiment Analysis
- 2.2. Use Cases of Sentiment Analysis
- 2.3. Challenges of Processing Natural Language
- 2.3.1. Negation
- 2.3.2. Deontic Irrealis
- 2.3.3. Languages
- 2.3.4. Emoticons, Acronyms, and Further Improvements
- 2.4. Domain-specific Language
- 2.5. Algorithmic Principles of Sentimental Analysis
- 2.6. Measuring Sentiment Analysis Accuracy
- 2.6.1. Precision, Recall and Accuracy
- 2.6.2. F-score
- 2.6.3. Supervised Machine-Learning Algorithms
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This master thesis explores the application of near-real-time sentiment analysis to understand competitive market behavior. The main objective is to analyze customer opinions in various markets using sentiment analysis, a tool for extracting emotions from text. The system is designed to be scalable for massive input directly from social networks and provides near-real-time results. Key themes include:- Analyzing customer sentiment in various markets.
- Developing a scalable server architecture for sentiment analysis.
- Implementing commercial use cases for sentiment analysis.
- Examining the relationship between market maturity, emotional intensity, and company performance.
Zusammenfassung der Kapitel (Chapter Summaries)
- Chapter 1: Introduction This chapter introduces the thesis's goal, which is to utilize near-real-time sentiment analysis to understand competitive market behavior. It also provides an outline of the subsequent chapters.
- Chapter 2: Sentiment Analysis This chapter delves into the concept of sentiment analysis, exploring its goals, use cases, and challenges. It discusses specific challenges like negation, deontic irrealis, language variations, and emoticons/acronyms. The chapter also examines domain-specific language, algorithmic principles of sentiment analysis, and methods for measuring its accuracy.
Schlüsselwörter (Keywords)
This master thesis focuses on the analysis of competitive market behavior using near-real-time sentiment analysis. Key concepts include: sentiment analysis, market behavior, customer opinions, social networks, server architecture, commercial use cases, emotional intensity, market maturity, and company performance.- Quote paper
- Norman Peitek (Author), 2014, Exploration of Competitive Market Behavior Using Near-Real-Time Sentiment Analysis, Munich, GRIN Verlag, https://www.grin.com/document/286583
