Sentiment Analysis Research Paper

Introduction

This repository contains the code and resources for our research paper on Sentiment Analysis. Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to determine whether data is positive, negative, or neutral. This research focuses on analyzing sentiment from various data sources using advanced machine learning algorithms.

Table of Contents

• Introduction
• Dataset
• Methodology
• Results
• Conclusion
• License

Dataset

We used a combination of publicly available sentiment datasets

Each dataset contains labeled data that indicates whether the sentiment of a given text is positive, negative, or neutral.

Methodology

Our methodology consists of the following steps:

1. Data Preprocessing: Cleaning and preparing the data for analysis.
2. Feature Extraction: Extracting relevant features from the text using techniques such as TF-IDF, word embeddings, etc.
3. Model Training: Training various machine learning models including Logistic Regression, SVM, and Neural Networks.
4. Evaluation: Evaluating the performance of the models using metrics such as accuracy, precision, recall, and F1-score.

Results

Our analysis yielded the following key findings:

• Random Forest Classifier: Achieved an accuracy of 84.92% with a precision of 83% and recall of 89%.
• Naive Bayes Theorem: Achieved an accuracy of 82.01% with a precision of 85% and recall of 78%.
• SVM : Achieved an accuracy of 85.98% with a precision of 87% and recall of 85%.

Detailed results and visualizations can be found in the RESULTS directory.

Conclusion

Our research demonstrates that Financial Market is greatky affected by the sentiment of news .

Installation

To replicate our results, follow these steps:

1. Clone the repository:

   git clone https://github.com/jheelamH/sentiment-analysis-research
   cd sentiment-analysis-research

2. Create a virtual environment and activate it:

   python3 -m venv env
   source env/bin/activate  # On Windows, use `env\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To run the sentiment analysis script, use the following command:

python sentiment_analysis.py --input data/input_file.txt --output results/output_file.txt