README.md

---
license: mit
language:
- en
- vi
metrics:
- accuracy
- f1
- recall
- precision
pipeline_tag: text-classification
tags:
- analysis
- sentiment
- text-classification
---
# Sentiment Analysis Using LSTM and CNN
This project implements a hybrid deep learning model combining **Long Short-Term Memory (LSTM)** networks and **Convolutional Neural Networks (CNN)** for sentiment analysis. The architecture leverages the strengths of both LSTM and CNN to process textual data and classify sentiments effectively.

---

## Model Architecture
![image](Overall.png)
The architecture consists of two parallel branches that process the input text sequences and merge their outputs for final classification:

### **Branch 1: CNN-Based Processing**
1. **Embedding Layer**: Converts input sequences into dense vector representations.
2. **Conv1D + Activation**: Extracts local features from the text using convolutional filters.
3. **MaxPooling1D**: Reduces the spatial dimensions while retaining the most important features.
4. **BatchNormalization**: Normalizes the activations to stabilize and accelerate training.
5. **Conv1D + MaxPooling1D + BatchNormalization**: Repeats the convolution and pooling process to extract deeper features.
6. **Flatten**: Converts the 2D feature maps into a 1D vector.

### **Branch 2: LSTM-Based Processing**
1. **Embedding Layer**: Similar to the CNN branch, converts input sequences into dense vector representations.
2. **Bidirectional LSTM**: Captures long-term dependencies in the text by processing it in both forward and backward directions.
3. **LayerNormalization**: Normalizes the outputs of the LSTM layer.
4. **Bidirectional GRU**: Further processes the sequence with Gated Recurrent Units for efficiency.
5. **LayerNormalization**: Normalizes the GRU outputs.
6. **Flatten**: Converts the sequence outputs into a 1D vector.

### **Merging and Classification**
1. **Concatenate**: Combines the outputs of the CNN and LSTM branches.
2. **Dense Layers with Dropout**: Fully connected layers with ReLU activation and dropout for regularization.
3. **Output Layer**: A dense layer with a softmax activation function to classify the sentiment into three categories: Positive, Neutral, and Negative.

---

## Why LSTM + CNN for Sentiment Analysis?

### **LSTM Strengths**
- LSTMs are well-suited for capturing long-term dependencies in sequential data, such as text.
- They excel at understanding the context and relationships between words in a sentence.

### **CNN Strengths**
- CNNs are effective at extracting local patterns and features, such as n-grams, from text data.
- They are computationally efficient and can process data in parallel.

### **Hybrid Approach**
By combining LSTM and CNN, the model benefits from:
- **Contextual Understanding**: LSTM captures the sequential nature of text.
- **Feature Extraction**: CNN identifies important local patterns.
- **Robustness**: The merged architecture ensures better generalization and performance on sentiment classification tasks.

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## Applications
This model can be used for:
- Social media sentiment analysis (e.g., Twitter, Reddit).
- Customer feedback classification.
- Opinion mining in reviews and surveys.

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## Training and Evaluation
The model is trained on labeled datasets with text and sentiment labels. It uses:
- **Sparse Categorical Crossentropy** as the loss function.
- **AdamW Optimizer** for efficient training.
- **Early Stopping** and **Model Checkpoints** to prevent overfitting and save the best model.

The performance is evaluated using metrics like accuracy, confusion matrix, and classification report.

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## Conclusion
The hybrid LSTM + CNN architecture provides a powerful framework for sentiment analysis, combining the strengths of sequential modeling and feature extraction. This approach is versatile and can be adapted to various text classification tasks.

## Lisence
MIT Lisence