Sentiment Analysis using Different Approaches

Sentiment analysis, also known as opinion mining, is a technique used in natural language processing (NLP) to determine whether a piece of text expresses positive, negative, or neutral sentiment. It plays a crucial role in understanding customer opinions, feedback, and emotions in various industries.

This project explores four distinct approaches to performing sentiment analysis, showcasing the evolution and versatility of machine learning techniques in NLP.

Approaches

1. Neural Bag of Words (NBOW)

The Neural Bag of Words model simplifies text processing by representing sentences as a "bag" of words without considering their order. Each word is mapped to an embedding, and these embeddings are averaged to produce a fixed-length vector, which is then passed through a neural network for sentiment classification.

• Advantages: Fast and simple, effective for basic tasks.
• Limitations: Ignores word order and complex syntactic relationships.

2. Recurrent Neural Networks (RNN)

Recurrent Neural Networks process text sequentially, preserving the order of words. This enables the model to capture dependencies between words, making it suitable for analyzing context and meaning in sentences.

• Advantages: Can handle sequential data and capture temporal dependencies.
• Limitations: May suffer from vanishing gradients in long sequences.

3. Convolutional Neural Networks (CNN)

While CNNs are traditionally used in image processing, they can be applied to text by using convolutional layers to capture local patterns in word embeddings. These patterns often correspond to important n-grams in the text.

• Advantages: Efficient in capturing local features like key phrases.
• Limitations: May not capture long-term dependencies effectively.

4. Transformers

Transformers, like BERT or GPT, revolutionized NLP by introducing attention mechanisms that consider relationships between all words in a sentence, regardless of their distance. They excel in understanding context and are widely used in state-of-the-art sentiment analysis systems.

• Advantages: High accuracy and ability to capture global context.
• Limitations: Computationally expensive and resource-intensive.

How to Access the Code

All the Colab notebooks for these approaches are available in this Google Drive folder.

Dependencies

To run the notebooks, ensure you have the following installed:

• Python 3.8+
• PyTorch or TensorFlow (depending on the approach)
• Hugging Face Transformers library (for Transformer-based models)
• NumPy, pandas, and scikit-learn for preprocessing and evaluation

Usage

1. Clone this repository or download the Colab notebooks from the Google Drive folder.
2. Open the desired notebook in Google Colab.
3. Follow the instructions in the notebook to preprocess the data, train the model, and evaluate its performance.

Acknowledgments

This project is inspired by advancements in NLP and aims to compare various approaches to sentiment analysis. Special thanks to the open-source community for providing pre-trained models and tools that make projects like this possible.

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Feel free to reach out for any questions or feedback. Happy coding!