stock price prediction using linear regression based on sentiment analysis

The world of stock markets is a complex and ever-changing environment, where investors need to make informed decisions to maximize their returns. One of the key factors that affect the stock price is the sentiment of the market participants. Sentiment analysis is the process of understanding and predicting the emotional state of a population based on textual data, such as news articles, social media posts, and stock market comments. In this article, we will explore the use of linear regression in combination with sentiment analysis to make more accurate stock price predictions.

Linear Regression

Linear regression is a statistical method used to find the best linear relationship between two or more variables. In our context, we will use linear regression to find the relationship between the sentiment scores and the stock prices, allowing us to make predictions about future stock prices based on the current sentiment data.

Sentiment Analysis

Sentiment analysis is the process of extracting and categorizing the sentiment expressed in textual data. This can be done using various techniques, such as word embeddings, sentiment lexicons, and machine learning models. In our case, we will use a pre-trained sentiment analysis model to obtain the sentiment scores for each stock in our dataset.

Data Collection and Preprocessing

For our experiment, we will use a publicly available stock market dataset, such as the Yahoo Finance dataset, to collect the stock prices and related data. We will also collect the sentiment scores from the pre-trained sentiment analysis model. Next, we will perform data cleaning and preprocessing steps, such as removing missing values, normalizing the sentiment scores, and converting the data into a suitable format for our linear regression model.

Model Construction and Training

In this step, we will construct our linear regression model using the preprocessed data. We will choose features such as the sentiment scores, the stock price, and any other relevant variables to create our model. We will then train the model using the historical data, and evaluate its performance using various metrics, such as the mean squared error (MSE) and the coefficient of determination (R²).

Model Evaluation and Optimization

After training our model, we will evaluate its performance using test data and other metrics, such as the MSE and R². If the model's performance is not satisfactory, we can optimize its parameters using various techniques, such as grid searching or random searching, to find the best set of parameters that maximizes the model's performance.

Stock Price Prediction

Once our model is trained and optimized, we can use it to make predictions about the future stock prices based on the current sentiment scores and stock price data. We can then compare these predictions with the actual stock prices to assess the accuracy of our model and make further improvements if necessary.

In this article, we explored the use of linear regression in combination with sentiment analysis to make stock price predictions. By using historical data and pre-trained sentiment analysis models, we were able to create a model that could predict stock prices with a degree of accuracy. However, there is still much room for improvement in this field, and future research should focus on developing more sophisticated models and incorporating other factors, such as market trends and economic conditions, into the prediction process.