This project focuses on sentiment analysis, a Natural Language Processing (NLP) task aimed at determining the sentiment or emotion expressed in text data. In this project, we aim to classify text reviews as either positive or negative sentiment using machine learning techniques, with a particular focus on deep learning.

This project utilizes several Python libraries for data processing, feature extraction, model development, and evaluation. The main libraries used in this project include:

• pandas: Used for data manipulation and handling of tabular data.
• re: Provides regular expression operations for text preprocessing.
• nltk (Natural Language Toolkit): A library for natural language processing tasks such as tokenization, stopwords removal, and lemmatization.
• gensim.downloader: Used to access pre-trained Word2Vec embeddings.
• numpy: Essential for numerical operations and handling arrays.
• matplotlib and seaborn: Libraries for data visualization and plotting.
• tensorflow: A deep learning framework for building and training machine learning models.
• bs4 (Beautiful Soup): A library for parsing HTML and XML documents.
• sklearn (Scikit-Learn): Used for splitting data into training and testing sets.

The project follows a typical machine learning project structure:

• Data Collection: Data was obtained from [source] and stored in a CSV file.

• Data Preprocessing: The collected data was preprocessed to prepare it for model training. This included text cleaning (HTML tag removal, lowercase conversion), tokenization, special character removal, and stopwords removal.

• Feature Extraction: Word2Vec embeddings were used to convert text data into numerical feature vectors.

• Model Development: A sequential deep learning model was built using TensorFlow/Keras. The model architecture consists of LSTM layers, dense layers, and a final sigmoid layer for sentiment classification.

• Model Training: The model was trained on the training dataset, and its performance was evaluated.

• Model Evaluation: The model's performance was assessed using various evaluation metrics, including accuracy, precision, recall, F1-score, and ROC-AUC.

• Data Visualization: Data visualization techniques were employed to gain insights into the dataset and model performance.

• Results: The results of the sentiment analysis model, including its accuracy and evaluation metrics, were documented.

To run this project, follow these steps:

1. Clone the repository to your local machine.
2. Ensure you have the required libraries installed. You can use pip install -r requirements.txt to install them.
3. Execute the Jupyter Notebook or Python script for data preprocessing, feature extraction, model development, and evaluation.

This project serves as a starting point for sentiment analysis. There are several ways to improve it:

• Experiment with different deep learning architectures, hyperparameters, and embeddings.
• Handle class imbalance issues if present in the dataset.
• Explore ensemble methods for model improvement.
• Collect a larger and more diverse dataset for better generalization.