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Twitter Sentiment Analysis – NLP Classification Model
An NLP model that classifies tweet sentiment using Python, NLTK, Scikit-learn, and TF-IDF feature extraction for social media trend analysis.
The Problem
Social media trend analysis requires efficiently classifying large volumes of tweet text by sentiment — positive, negative, or neutral — to support marketing, policy, and research decisions.
The Solution
Built a sentiment analysis pipeline using Python and NLTK for preprocessing (tokenization, stemming, lemmatization), TF-IDF feature extraction, and Scikit-learn classification algorithms. The model classifies tweets by sentiment to enable social media trend analysis.
The Outcome
Achieved improved accuracy in sentiment classification through systematic preprocessing and TF-IDF feature engineering, delivering a reusable NLP pipeline for social media trend analysis.
Project Details
Twitter Sentiment Analysis is an NLP project built with Python, NLTK, Scikit-learn, and Pandas. The pipeline covers data preprocessing (tokenization, stemming, lemmatization), TF-IDF feature extraction, and training of classification algorithms to predict tweet sentiment. The goal is accurate social media trend analysis.
Technologies
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