Natural Language Processing-Based Financial Time Series Forecasting: Utilizing Sentiment Analysis for Improved Stock Price Prediction - Albert Ntumba Nkongolo; Yae Olatoundji Gaba; Kafunda Katalay Pierre; Esther Matendo Mabela; Ben Mbuyi Mpumbu (Journal of Innovation Information Technology and Application)
This study explores the application of natural language processing (NLP) techniques in financial time series forecasting, specifically in predicting stock prices. Historical stock price data and textual data from financial news articles and social media sources were collected, and TextBlob was used to obtain sentiment indices from the textual data. A hybrid model combining NLP techniques with LSTM (Long Short-Term Memory) neural networks was developed, and the methodology involved preprocessing and analyzing textual data using sentiment analysis with TextBlob and integrating the sentiment indices with historical stock price data for forecasting with LSTM. The LSTM model achieved a performance of 89.6 percent precision and outperformed traditional time series forecasting models in terms of accuracy and reliability. The results demonstrate that incorporating sentiment indices obtained through NLP significantly enhances the predictive performance of stock price forecasting models, and the study highlights the potential of NLP techniques, particularly sentiment analysis with TextBlob, in conjunction with LSTM neural networks, to improve the accuracy of financial time series forecasting, specifically in predicting stock prices.