Stock Market Sentiment Analysis Using Python & Machine Learning

Computer Science

30 Jul 202042:44

EducationalLearning

32 Likes 10 Comments

TLDRIn this informative video, the creator demonstrates how to predict stock price fluctuations using sentiment analysis and machine learning. The process involves analyzing news headlines for the Dow Jones Industrial Average and leveraging tools like Google Colab, TextBlob, and VaderSentiment. The creator guides viewers through data preparation, model training with Linear Discriminant Analysis, and evaluation, achieving an 84% accuracy rate in predictions. The video is an engaging blend of Python programming and financial analysis, offering insights into the potential of AI in stock market forecasting.

Takeaways

🤖 The video is about using Python and machine learning for stock price prediction based on news headlines.
📈 The process involves sentiment analysis to determine the writer's attitude towards a topic, which can be positive, negative, or neutral.
🔍 Google's Colab platform is used for easy Python programming and running the code without needing to set up a local environment.
📊 The script uses libraries like Pandas, NumPy, TextBlob, and VaderSentiment for data manipulation and analysis.
📄 Data is loaded from CSV files containing news headlines and stock prices of the Dow Jones Industrial Average index.
🔧 Data preprocessing includes merging datasets, cleaning text data, and combining multiple news headlines into a single column.
📊 Sentiment analysis is performed using TextBlob and VaderSentiment to calculate subjectivity, polarity, and sentiment intensity scores.
🛠️ A machine learning model is created and trained using the cleaned and processed data, specifically Linear Discriminant Analysis.
📊 The model's accuracy is evaluated using a test dataset, achieving an approximate accuracy of 84%.
🎯 The video serves as an educational exercise to demonstrate how machine learning can be applied to financial data and sentiment analysis.
📢 The video creator encourages viewers to ask questions, share the content, and acknowledges Patreon supporters.

Q & A

What is the main focus of the video?
-The main focus of the video is to demonstrate how to predict the increase or decrease in a company's stock price based on top news headlines using sentiment analysis and machine learning in Python.
What is sentiment analysis?
-Sentiment analysis is the computational process of identifying and categorizing opinions expressed in a piece of text to determine the writer's attitude towards a particular topic, product, or subject as positive, negative, or neutral.
Which platform is used for easy Python programming in the video?
-Google's Colab platform (colab.research.google.com) is used for easy Python programming in the video.
What libraries are imported for the program?
-The libraries imported for the program include pandas, numpy, TextBlob, re, vader sentiment, sklearn.model_selection, sklearn.metrics, and sklearn.discriminant_analysis.
How are the datasets for news headlines and stock prices loaded?
-The datasets are loaded by using the 'import files' function from google.colab and uploading the CSV files containing the news headlines and stock prices for the Dow Jones Industrial Average index.
What is done with the news headlines data?
-The news headlines data is combined into a single column called 'combined_news' and then cleaned to remove unwanted characters such as 'b's, quotation marks, and slashes.
How are subjectivity and polarity calculated for the text?
-Subjectivity and polarity are calculated for the text using the TextBlob library's 'sentiment.subjectivity' and 'sentiment.polarity' methods, respectively.
What is the Sentiment Intensity Analyzer (SIA) used for?
-The Sentiment Intensity Analyzer (SIA) is used to calculate sentiment scores, including compound, negative, neutral, and positive scores, which help in determining the overall sentiment of the text.
How is the machine learning model created and trained in the video?
-The machine learning model is created using the Linear Discriminant Analysis from the sklearn.discriminant_analysis library and trained with the feature dataset (x_train) and the target dataset (y_train).
What is the accuracy of the model as reported in the video?
-The model's accuracy, as reported in the video, is approximately 84%.
How can the model be potentially used?
-The model can be potentially used to predict whether the price of a stock will go up or down based on the sentiment analysis of news headlines.

Outlines

00:00

🚀 Introduction to Stock Price Prediction with Machine Learning

The video begins with an introduction to a machine learning project aimed at predicting stock price movements based on sentiment analysis of news headlines. The creator explains the concept of sentiment analysis and its importance in understanding the writer's attitude towards a subject. The video setting is Google's Colab, which is highlighted for its ease of use in Python programming. The creator guides viewers on how to set up a new notebook and provides a brief overview of the steps to be followed, including installing necessary packages and importing libraries.

05:01

📊 Loading and Preparing the Data for Analysis

In this section, the creator focuses on loading the datasets required for the analysis, which include news headlines and stock prices from the Dow Jones Industrial Average index. The process of uploading files in Colab and reading them into pandas DataFrames is demonstrated. The creator also explains the structure of the datasets, highlighting the 'label' column that indicates whether the stock price increased or decreased. The video pauses to allow for the data loading process to complete before continuing.

10:03

🔄 Merging and Cleaning the Dataset

The creator proceeds to merge the two datasets based on the date column, resulting in a combined dataset that includes both news headlines and stock price information. A new column is created to store the combined news headlines. The creator then addresses the issue of cleaning the data, removing unwanted characters such as quotation marks and slashes to prepare the dataset for further analysis. The process of cleaning the headlines is demonstrated, and the results are shown to ensure the data is ready for the next steps.

15:03

📈 Analyzing Sentiment with TextBlob and VaderSentiment

This part of the video script details the creation of functions to calculate subjectivity and polarity from the text data using TextBlob. The creator explains the significance of these metrics in sentiment analysis. The video demonstrates how to apply these functions to the combined news headlines, resulting in new columns for subjectivity and polarity within the dataset. The creator also introduces the Sentiment Intensity Analyzer from the vaderSentiment library to assess the sentiment scores of the text.

20:04

🏗️ Building and Training the Predictive Model

The creator moves on to the model building phase, where a list of relevant columns for the prediction model is compiled. The dataset is then prepared by creating feature and target datasets, with the latter indicating whether the stock price increased or decreased. The data is split into training and testing sets, with 80% allocated for training the model. A Linear Discriminant Analysis model is trained using the training data, and the creator provides a brief overview of the process.

25:05

📊 Evaluating the Model's Performance

The video concludes with the evaluation of the trained model's performance. Predictions are made using the test dataset, and the results are compared with the actual outcomes (y_test dataset). The creator calculates and presents the model's accuracy, which stands at around 84%. The video ends with a classification report that provides further insight into the model's performance across different classes. The creator encourages viewers to ask questions and engage with the content, and thanks the supporters of the channel.

Mindmap

Keywords

💡Python

Python is a high-level, interpreted programming language known for its readability and ease of use. In the video, Python is used as the primary programming language for machine learning and sentiment analysis to predict stock price movements based on news headlines.

💡Machine Learning

Machine learning is a subset of artificial intelligence that involves the use of statistical models and algorithms to enable systems to learn from and make predictions or decisions based on data. In the context of the video, machine learning is employed to analyze sentiment from news headlines to predict stock price trends.

💡Sentiment Analysis

Sentiment analysis, also known as opinion mining, is the process of determining the emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions, and emotions expressed within a text. In the video, sentiment analysis is performed on news headlines to predict stock market movements.

💡Google Colab

Google Colab is a cloud-based platform for machine learning, which allows users to write and execute Python code in their browser, with the ability to use free GPU support. In the video, the speaker uses Google Colab to write and run Python code for the machine learning project.

💡Pandas

Pandas is an open-source data analysis and manipulation library for Python, providing data structures and functions needed to manipulate structured data. In the video, Pandas is used to handle and process the dataset containing news headlines and stock prices.

💡Numpy

Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. It is used in the video for numerical computations involved in machine learning.

💡TextBlob

TextBlob is a Python library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. In the video, TextBlob is used to perform sentiment analysis on news headlines.

💡Vader Sentiment

Vader Sentiment is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media. It is used in the video to install and utilize for sentiment analysis, providing compound scores that indicate the overall sentiment of a given text.

💡Sklearn

Scikit-learn, or Sklearn, is a Python module for machine learning built on top of SciPy. It features various classification, regression, and clustering algorithms, as well as tools for model validation and data preprocessing. In the video, Sklearn is used for model selection, training, and evaluation.

💡Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis, or LDA, is a statistical technique used to find a linear combination of features that characterizes or separates two or more classes of objects or events. It is employed in the video as a classification algorithm to predict the increase or decrease in stock prices based on sentiment analysis.

💡Data Cleaning

Data cleaning is the process of detecting and correcting (or deleting) incorrect or inconsistent data in an analytical dataset. In the video, the speaker performs data cleaning to remove unwanted characters and punctuation from the news headlines to prepare the data for analysis.

💡Model Evaluation

Model evaluation is the process of assessing the performance of a machine learning model by comparing its predictions to the actual outcomes. The speaker uses metrics such as accuracy score and classification report to evaluate how well the model can predict stock price movements based on sentiment analysis.

Highlights

The video introduces a method to predict stock price changes using sentiment analysis and machine learning.

Sentiment analysis is used to determine the writer's attitude towards a topic, product, or subject as positive, negative, or neutral.

Google's Colab is used as the platform for easy Python programming and running the code.

The video demonstrates installing necessary dependencies like 'vader sentiment' for sentiment analysis.

Libraries used include pandas, numpy, textblob, re, and various modules from sklearn.

Data is loaded from CSV files containing news headlines and stock prices of the Dow Jones Industrial Average.

The data is cleaned to remove unwanted characters and combine news headlines into a single column.

Functions are created to calculate subjectivity and polarity from the text data.

Sentiment scores (compound, negative, neutral, positive) are calculated using the Sentiment Intensity Analyzer.

The sentiment scores are added to the dataset for further analysis.

A subset of the data is selected for model training, excluding stock price columns.

The data is split into training (80%) and testing (20%) datasets.

A Linear Discriminant Analysis model is trained and used for prediction.

The model's predictions are compared with the actual data to evaluate its performance.

The video concludes with the model achieving an accuracy of about 84%.

The presenter encourages viewers to learn from the exercise and apply the model for stock price prediction.

The video is intended as an educational resource and not a definitive guide to stock trading.

Transcripts

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Related Tags

Stock Prediction Sentiment Analysis Machine Learning Python Programming Data Analysis Financial Forecasting Text Processing Google Colab Linear Discriminant Analysis Investment Strategy