Enhancing Investment Strategies with NLP-Driven Sentiment Analysis from Social Media and News Sources

Abstract

In recent years, Natural Language Processing (NLP) has emerged as a powerful tool in extracting meaningful insights from large volumes of unstructured data. This research explores the application of sentiment analysis, an NLP technique, to the field of investment decision-making. By analyzing user opinions and news articles related to specific investment assets, this study proposes a tool that helps investors gauge market sentiment in real-time. The core functionality of the application relies on gathering and processing data from social media platforms like Twitter and various news websites through web scraping techniques. Once the data is collected, it undergoes a series of pre-processing steps, including tokenization, normalization, and removal of stopwords, followed by sentiment classification using algorithms like TextBlob. The system generates visual representations, such as bar charts, word clouds, and trend graphs, to provide investors with a consolidated view of market sentiment. The results of the sentiment analysis are then summarized in a comprehensive report, enabling users to make more informed investment choices. The research demonstrates that by leveraging NLP, investors can gain a macro perspective on market dynamics, reducing information overload and making more data-driven decisions. Future enhancements of the tool may include incorporating additional investment types, refining sentiment analysis models, and expanding the scope of the web crawler to gather data from a wider range of financial news sources.