Sentiment Analysis of Social Media Comments Using Python Project Overview This project focuses on sentiment analysis of social media data, specifically analyzing user comments from Twitter and Instagram. With the vast amount of textual data generated daily on these platforms, sentiment analysis helps identify user opinions, preferences, and trends. By examining the sentiment of tweets and Instagram comments, we can uncover valuable insights into public opinion and understand user engagement.

Objective The primary objective of this project is to analyze sentiment in social media comments by calculating two key metrics:

Polarity: Measures the sentiment orientation, with values ranging from -1 (negative) to 1 (positive). Subjectivity: Indicates the degree of objectivity in the text, with values from 0 (objective) to 1 (subjective). These metrics allow us to gauge the emotional tone behind user comments, supporting brands and businesses in understanding public perception and enhancing user satisfaction based on feedback.

Data Sources and Tools Data Sources: Tweets and Instagram comments. Libraries: TextBlob for calculating polarity and subjectivity. Pandas for data handling. Matplotlib or Seaborn for visualizing sentiment distribution and trends. This project provides a structured approach to understanding sentiment on social media, offering a foundation for further analysis and improvements in digital marketing and customer relations.