Sentiment Analysis

In the domain of Sentiment Analysis within Natural Language Processing (NLP), I significantly improved model accuracy by 10% through strategic dataset expansion and the incorporation of few-shot inference techniques. Utilizing the Bi-Directional Long Short-Term Memory (BiLSTM) algorithm enhanced sentiment analysis results. Employing advanced NLP preprocessing techniques, such as regular expression cleaning, tokenization, stopword removal, and lemmatization, optimized input data quality.

Comprehensive preprocessing steps involved regular expression cleaning, tokenization, stopwords removal, and lemmatization to refine data for sentiment analysis. Integration of a Word Embedding layer facilitated word vectorization, contributing to a nuanced language understanding. The approach demonstrated a holistic understanding of NLP methodologies, spanning from preprocessing to model deployment.

This endeavor showcases expertise in improving sentiment analysis through diverse strategies, including advanced algorithms, preprocessing techniques, and model deployment, highlighting a comprehensive grasp of NLP in sentiment analysis applications.