The use of machine learning techniques to analyze customer feedback has the potential to provide valuable insights for businesses. In this thesis, we present a tool for extracting Voice of the Customer (VoC) data from tweets and analyzing it using natural language processing (NLP) techniques. The tool consists of four macro steps that are flexible and can be run in parallel, allowing for efficient and effective data extraction and analysis. We propose two architectures one for Sentiment Analysis and one for Named Entity Recognition (NER), and demonstrate that our approach outperforms the baselines reported in the literature in both tasks. In the case of Sentiment Analysis, we address the data and conceptual shift after demonstrating it with the Human Level Performance Analysis.