Abstract:
Forecasting stock trends guide investment management, financial policy, and the
country’s economic growth. Investor-generated textual information has impacted
stock movements across media channels in recent years. Most sentiment index
studies weigh linguistic content equally. Such studies ignore that the sentiment
index’s impact on the stock market decreases over time. This study analyses
stock indices using dual classifier coupling and sentiment analysis. A dual clas sifier is created by combining two popular classifiers, Decision Tree (DT) with
Convolution Bi-Directional Gated Recurrent Unit (GRU). The proposed model is
tested using Reliance Industries shares. The adjusted sentiment index improved
overall accuracy in the Reliance Industries stock news sentiment analysis case
study by 84.12 percent. The investor sentiment indicator improves stock index
trend prediction, as shown by a 3.16 RMSE (Root Mean Squared Error) and
0.97 R2(Coefficient of determination) reduction. The adjusted sentiment index
improves predicted accuracy considerably. The investors’ sentiments improve the
overall results in Reliance Industries’ stock price prediction with our fusion of pro posed VADER (Valence Aware Dictionary and sEntiment Reasoner) and CNN
+ BDGRU models compared to benchmark models
