Twitter Sentiment Analysis using NLP and Machine Learning to classify sentiments.

This project focuses on analyzing sentiment from tweets using Natural Language Processing (NLP) techniques. Sentiment analysis involves classifying tweets as positive, neutral, or negative, providing actionable insights into public opinion and trends. This work demonstrates expertise in Python, data preprocessing, statistical modeling, as well as practical applications of machine learning models.

#Features

Data Collection: Tweets collected using Twitter's API (or dataset source, if applicable).

Text Preprocessing: Tokenization, stop-word removal, stemming, and lemmatization.

Sentiment Classification: Application of supervised learning models to classify tweets.

#Tools & Technologies Programming Languages: Python

NLP Techniques: Sentiment Analysis, Text Preprocessing, Tokenization

Machine Learning Libraries: Scikit-learn, NLTK, Pickle

#Project Highlights

Developed an effective sentiment classification pipeline for real-world applications.

Leveraged Named Entity Recognition (NER) to identify critical topics or entities in tweets.

Presented insights that can guide social media marketing campaigns, customer satisfaction initiatives, or trend analysis.