This critical essay, named “NLP-Based Sentiment Analysis for Trading Strategy Building,” is a review of Chapter 10 of the book Machine Learning and Data Science Blueprints for Finance written by H. Tatsat et al.
This essay will start with a brief introduction of relevant background knowledge that is later required in the following section, which includes machine learning, natural language processing, and their applications in finance-related fields.
The section Reviewing the Case Study provides a detailed review of Case Study 1 in Chapter 10, which is a news-based sentiment analysis method used for building trading strategies. This section analyzes different stages in the authors’ workflow, from data preprocessing to trading strategy making, and provides comments on which designs are good and what could be improved regarding the design itself and/or the readability.
The Conclusion section will finally review the strengths and weaknesses of the overall solution provided by the author and make a few suggestions on future improvements.
The full critical review is available here.