TY - GEN
T1 - Hybrid Dataset Trained LSTM Model for Forecasting Stock Market Trends
T2 - 4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023
AU - Bongale, Anupkumar M.
AU - Gandhi, Heeral
AU - Upadhyaya, Eshita
AU - Sangwan, Akshay
AU - Anand, Aniket
AU - Dharrao, Deepak
AU - Krishna, Raguru Jaya
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - This research paper proposes a hybrid method that combines historical stock data with sentiment analysis of Twitter data to predict the stock market index. Twitter has become a popular platform for expressing public opinion and sentiment about various events, including the stock market. The study employs various steps such as preprocessing, hyperparameter tuning, and Long Short-Term Memory (LSTM) modeling to analyze the sentiment of tweets and forecast Nifty 50 index values. The proposed approach provides a promising direction for predicting stock market indices, particularly based on short-term prediction using social media data. The hybrid dataset combining historical stock index values with Twitter text data leads to improved accuracy in forecasting stock market indices and trends.
AB - This research paper proposes a hybrid method that combines historical stock data with sentiment analysis of Twitter data to predict the stock market index. Twitter has become a popular platform for expressing public opinion and sentiment about various events, including the stock market. The study employs various steps such as preprocessing, hyperparameter tuning, and Long Short-Term Memory (LSTM) modeling to analyze the sentiment of tweets and forecast Nifty 50 index values. The proposed approach provides a promising direction for predicting stock market indices, particularly based on short-term prediction using social media data. The hybrid dataset combining historical stock index values with Twitter text data leads to improved accuracy in forecasting stock market indices and trends.
UR - https://www.scopus.com/pages/publications/85206393201
UR - https://www.scopus.com/pages/publications/85206393201#tab=citedBy
U2 - 10.1007/978-981-97-3690-4_30
DO - 10.1007/978-981-97-3690-4_30
M3 - Conference contribution
AN - SCOPUS:85206393201
SN - 9789819736898
T3 - Smart Innovation, Systems and Technologies
SP - 399
EP - 412
BT - Smart Systems
A2 - Somani, Arun K.
A2 - Mundra, Ankit
A2 - Gupta, Rohit Kumar
A2 - Bhattacharya, Subhajit
A2 - Mazumdar, Arka Prokash
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 October 2023 through 27 October 2023
ER -