Financial Text Sentiment Analysis

Introduction

Welcome to the Sentiment Analysis project! This project focuses on analyzing sentiment from text data, categorizing it into positive, negative, or neutral sentiments. By following a systematic process involving Exploratory Data Analysis (EDA), preprocessing, bag-of-words representation, and utilizing multiple machine learning models, this project aims to extract insights from textual content and understand the sentiment expressed within it.

Process Overview

1. Exploratory Data Analysis (EDA)

• Data Exploration: Understand the distribution of sentiment labels in the dataset.

2. Preprocessing

• Text Cleaning: Remove noise, special characters, and irrelevant symbols from the text.
• Tokenization: Split text into individual tokens (words or n-grams).
• Lowercasing: Convert text to lowercase for consistency.
• Stopword Removal: Eliminate common stopwords.
• Stemming/Lemmatization: Normalize words to their root form.
• Feature Engineering: Create additional features such as word counts or TF-IDF scores.

3. Bag of Words Representation

• Vectorization: Transform preprocessed text data into numerical vectors.
• Feature Extraction: Extract features from text data while preserving frequency information.
• Vocabulary Construction: Build a vocabulary of unique words or n-grams.

4. Multiple Machine Learning Models

• Model Selection: Choose from various machine learning algorithms.
• Model Training: Train each selected model on preprocessed and vectorized data.
• Hyperparameter Tuning: Optimize model hyperparameters.
• Model Evaluation: Evaluate performance using metrics like accuracy, precision, recall, F1-score, or ROC-AUC.

5. Evaluation

• Evaluation Metrics: Calculate various evaluation metrics to gauge model performance.
• Confusion Matrix: Analyze model predictions and misclassifications.

Usage

To use this sentiment analysis pipeline:

1. Clone the repository: git clone https://github.com/Avyukth1C/financial-text-sentiment.git
2. Unzip the files.
3. Import the .ipynb into Google Colab.
4. Import the dataset into your Google drive.[Change the file path in the code respectively]
5. Run the Jupyter notebook provided in the repository to Train the machine learning models using the prepared dataset.
6. Use the functions for real-time sentiment analysis.

Support

If you encounter any issues or have questions about the Sentiment Analysis project, please feel free to create an issue in the repository.create an issue.

License

This project is licensed under the MIT License.MIT License.