Idea: Sentiment Analysis
The primary goal is to develop a sentiment analysis model that can accurately classify the sentiment of text data, providing valuable insights into public opinion, customer feedback, and social media trends.
Data Set : link
Sentiment Analysis: Analyzing text data to determine the emotional tone, whether positive, negative, or neutral.
Natural Language Processing (NLP): Utilizing algorithms and models to understand and process human language.
Machine Learning Algorithms: Implementing models for sentiment classification, such as Support Vector Machines, Naive Bayes, or deep learning architectures.
Feature Engineering: Identifying and extracting relevant features from text data to enhance model performance.
Data Visualization: Presenting sentiment analysis results through effective visualizations for clear interpretation