Crowdfunding Campaigns. Success Prediction Through Natural Language Processing

Inhaltsverzeichnis (Table of Contents)

• Abstract
• Introduction
• Theoretical Foundation
• Data Mining Process
• Natural Language Processing
• Definition and Distinction
• Preprocessing and Feature Selection
• Vectorization of Words
• Architecture and Types of Artificial Neural Networks
• Feed Forward Neural Networks
• Convolutional Neural Networks
• Graph Neural Networks
• Recurrent Neural Networks
• Methods for Performance Evaluation
• Applied technologies
• Methodology
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling
• Convolutional Neural Networks
• Graph Neural Networks
• Bidirectional LSTM
• Evaluation of Results
• Convolutional Neural Network Results
• Category Prediction by Convolutional Neural Network
• Success Prediction by Convolutional Neural Network
• Graph Convolutional Neural Network Results
• Category Prediction by Graph Convolutional Neural Network
• Success Prediction by Convolutional Neural Network
• Bidirectional LSTM Results
• Category Prediction by Bidirectional LSTM
• Success Prediction by Bidirectional LSTM
• Discussion

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This thesis aims to examine whether information regarding the categorization and success prediction of crowdfunding campaigns can be generated using natural language processing (NLP) and data mining approaches. The goal is to provide valuable insights for founders setting up campaigns on platforms like Kickstarter and Indiegogo, enabling them to gain a competitive advantage.

• The application of NLP and data mining for analyzing crowdfunding campaigns.
• The use of artificial neural networks for campaign classification and success prediction.
• The influence of campaign category and founder focus on campaign success.
• The potential of data-driven approaches for supporting crowdfunding success.
• The challenges and limitations of applying NLP and data mining to crowdfunding data.

Zusammenfassung der Kapitel (Chapter Summaries)

The thesis delves into the theoretical foundation of data mining and NLP, exploring key concepts such as the CRISP-DM model, preprocessing techniques, vectorization methods, and various types of artificial neural networks. The methodology chapter details the data collection and preparation process, outlining the specific steps involved in utilizing convolutional neural networks, graph neural networks, and bidirectional LSTMs to achieve the desired categorization and success prediction objectives. The evaluation of results section provides a comprehensive analysis of the findings for each model, discussing the accuracy, performance, and limitations of each approach.

Schlüsselwörter (Keywords)

Crowdfunding, Natural Language Processing, Artificial Neural Networks, Data Mining, Campaign Classification, Success Prediction, Kickstarter, Indiegogo, Competitive Advantage, Data Analysis, Machine Learning, Deep Learning, Text Classification, Sentiment Analysis, Data Set, Campaign Focus, Category Influence.