For corporations, marketers, and legislators, knowing public sentiment is crucial in the fast-paced world of social media. Twitter is an excellent resource for sentiment analysis because of its millions of real-time chats. I recently performed a sentiment analysis study on Twitter data using Natural Language Processing (NLP), evaluating several machine learning models and producing an extensive classification report.
The project progressed as follows:
š¹ Gathering and Preparing Data: In order to examine public sentiment, I collected a dataset of tweets via Twitter's API with an emphasis on popular subjects. The initial stage was extensive text cleaning since unprocessed twitter data might be noisy:
Text Cleaning: Special characters, hashtags, and URLs were eliminated.
Divide tweets into distinct words using tokenization.
Lowercasing: For consistency, all terms were changed to lowercase.
