LSTM Sentiment Analysis

            Key Achievement: Built a Deep Learning model achieving an 88% F1 Score on unstructured customer review data. 
        

Python TensorFlow NLP

Project Overview

This project utilized Long Short-Term Memory (LSTM) neural networks to process sequential text data, capturing context and sentiment more effectively than traditional bag-of-words models.

The "What": Technical Methodology

I implemented a recurrent neural network architecture using Keras. The process involved text tokenization, sequence padding, and an embedding layer to map words into high-dimensional vectors. This allowed the model to understand the nuance and order of words in a sentence, leading to superior accuracy over standard machine learning models.

The "Why": Data Science Impact

In the field of Data Science, unstructured text is a massive untapped resource. By automating sentiment analysis, I empower organizations to convert subjective feedback into actionable data points. This project demonstrates the ability to scale customer insights and drive product improvements based on data-driven sentiment tracking.

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