LSTM_Based_Stock_Market_Prediction

Overview

This Python module defines a trading model that leverages sentiment analysis and Long Short-Term Memory (LSTM) neural networks to predict stock price movements based on public opinion extracted from Twitter data.

Dependencies

• pandas
• numpy
• matplotlib
• sklearn
• keras (LSTM, Dense, Dropout, Sequential from keras.layers)
• vaderSentiment (SentimentIntensityAnalyzer)

Data

The model requires three data frames:

• df_tweet: Tweets data.
• df_company_tweet: A mapping between tweets and companies.
• df_company: Company information.
• Download Raw Data: Google Drive

Class Model

The Model class contains all the methods needed to preprocess the data, perform sentiment analysis, train LSTM models, and plot results.

Initialization

Create an instance of the Model class by passing the required data frames as arguments:

model = Model(df_tweet, df_company_tweet, df_company)