This repository hosts a sentiment analysis project utilizing BERT (Bidirectional Encoder Representations from Transformers), a state-of-the-art neural network architecture for natural language processing tasks. The project focuses on classifying sentiment in textual data into positive, negative, or neutral categories using BERT's powerful contextual embeddings and attention mechanisms.
The dataset used in this project consists of a collection of textual data with associated sentiment labels. This dataset may include various sources such as customer reviews, social media posts, or movie reviews. Each sample in the dataset is labeled with its corresponding sentiment category, enabling the training of the sentiment analysis model.
The project structure is organized as follows:
- data: Contains the dataset used for training and testing the sentiment analysis model.
- notebooks: Includes Jupyter notebooks detailing the data preprocessing steps, fine-tuning of BERT, model development, and evaluation.
- src: Contains the Python code for training and deploying the sentiment analysis model using BERT.
- requirements.txt: Lists the necessary dependencies required to run the project.
Data preprocessing involves tokenizing the text, converting it into BERT-compatible input format, and performing any necessary text cleaning and normalization. BERT requires specific input formatting, including tokenization, adding special tokens for classification tasks, and padding/truncating sequences to a fixed length.
The main task in this project is fine-tuning the pre-trained BERT model for sentiment analysis. Fine-tuning involves training the model on the labeled dataset, adjusting the model's parameters to adapt to the specific sentiment classification task. This process allows BERT to learn task-specific features and nuances from the training data.
The sentiment analysis model is evaluated using appropriate metrics such as accuracy, precision, recall, and F1-score on a held-out test dataset. Additionally, visualizations and confusion matrices may be used to assess the model's performance across different sentiment categories.
To get started with the project, follow these steps:
- Clone this repository to your local machine.
- Navigate to the
notebooks. - Install the necessary dependencies by running:
pip install -r requirements.txt- Run the provided notebooks sequentially to replicate the analysis and understand the steps involved in fine-tuning BERT for sentiment analysis.
The project requires Python 3.11 along with various libraries such as pandas, numpy, transformers, torch, etc. These dependencies are listed in the requirements.txt file.
Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.
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