Enhancing Marketing Strategies through Sentiment Analysis, NLP, and Machine Learning.

In the contemporary business landscape, the convergence of Machine Learning (ML), Sentiment Analysis (SA), and Natural Language Processing (NLP) has revolutionized marketing strategies by offering insights into customer sentiments and preferences. This thesis delves into the utilization of these advanced technologies to amplify marketing activities, with a specific focus on Amazon customer reviews as a case study. The study's primary objective is to demonstrate how ML algorithms can extract valuable insights from the massive volume of customer reviews available on Amazon's platform. Through the application of NLP techniques, the textual data is processed, enabling sentiment analysis to gauge customer opinions accurately. The findings highlight the role of sentiment in influencing purchasing decisions and brand perception. By examining the interplay of ML, SA, and NLP, this thesis contributes to a deeper understanding of how businesses can harness these technologies to optimize marketing strategies. The insights derived from the Amazon customer reviews case study provide a roadmap for marketers to fine-tune their campaigns, enhance product offerings, and build stronger customer relationships.