A Bidirectional LSTM–Sentiment Fusion Framework for Dynamic Financial Market Prediction

Financial market prediction can be said to be a great challenge because of the intrinsic fluctuation, non-stationarity and multi-faceted influence of the economic indicators, world events, as well as the voter sentiment. Conventional models can easily miss the time dependence and emotional aspects inherent in market data, and the results in poor forecasting precision. The paper presents a sequence-based modelling with sentiment analysis based on textual information like news articles and social media, which incorporates a two-way LSTM-sentiment fusion framework. It discloses that sentiment integration discerns as well as polishes predictive results into alignment with temporal characteristics with real-time emotive drivers.