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README.md

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

requirements.txt

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+tensorflow-macos
+tensorflow-metal
+text_hammer
+scikit-learn
+setuptools
+wheel
+spacy
+bs4
+html5lib
+lxml