Neural Sentiment Analysis with LSTM

1. Objective

This experiment implements a Long Short-Term Memory (LSTM) network to classify text sequences into emotional states. The goal is to achieve high precision in identifying negative sentiment within customer feedback logs.

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2. Model Architecture

The network consists of:

• An Embedding layer
• Two stacked LSTM layers
• A Dense output layer with Softmax activation

🔧 Training Script

python
import tensorflow as tf
from tensorflow.keras.layers import LSTM, Dense, Embedding

# Defining the architecture
model = tf.keras.Sequential([
    Embedding(input_dim=5000, output_dim=128),
    LSTM(64, return_sequences=True),
    LSTM(32),
    Dense(3, activation='softmax')
])

model.compile(optimizer='adam', loss='categorical_crossentropy')

print("NETWORK_INITIALIZED_SUCCESSFULLY")

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3. Results & Logs

After 50 epochs, the model converged with a steady decrease in categorical cross-entropy.

Output
Epoch 50/50
loss: 0.1421 - accuracy: 0.9654 - val_loss: 0.2104 - val_accuracy: 0.9211
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Final Metrics:
Precision: 0.94
Recall: 0.91
F1-Score: 0.92

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4. Visual Analysis

Below is the confusion matrix generated during the evaluation phase:

(Confusion matrix image will be rendered here via Supabase / Markdown image upload)

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📌 End of Experiment Log