Bringing it to Life
As my model reached maturity, it was time to unleash its potential upon the world. I envisioned scenarios where it could be deployed, from sentiment analysis in social media monitoring to feedback analysis in customer service. Armed with Python scripts and APIs, I breathed life into my creation, eager to witness its impact on real-world applications.
# Perform sentiment analysis on new text
def predict_sentiment(text):
clean_text = preprocess_text(text)
encoded_text = tokenizer.texts_to_sequences([clean_text])
padded_text = pad_sequences(encoded_text, maxlen=max_length, padding=’post’)
sentiment = model.predict(padded_text)
return sentiment
text = “This movie was amazing!”
sentiment = predict_sentiment(text)
print(“Sentiment: {:.2f}”.format(sentiment[0][0]))
Conclusion:
Reflecting on my journey of building a sentiment analysis model fills me with pride and gratitude. It’s not just about the lines of code or the layers of neural networks—it’s about the passion, creativity, and determination that went into every step of the process. As I share my experiences with others, I hope to ignite the same spark of curiosity and drive that propelled me on this exhilarating journey of creation and discovery.
My Journey of Building a Sentiment Analysis Model with Python and TensorFlow
Embarking on the journey of building a sentiment analysis model has been an exciting and fulfilling experience for me. In this article, I’m thrilled to share the process I followed to create a sentiment analysis model using Python and TensorFlow, hoping it inspires others in their own AI endeavours.