MoodMapr is a sentiment analysis tool built using a Logistic Regression model combined with TF-IDF vectorization. Designed to deliver high accuracy, MoodMapr classifies text into positive or negative sentiment categories. This tool is ideal for analyzing customer reviews, social media posts, and textual feedback, providing valuable insights into the emotional landscape of text data.

1. Sentiment Analysis for Text Data:

   • Accurately classifies text into positive or negative sentiment categories.

2. Logistic Regression Model:

   • Utilizes a Logistic Regression model, known for its effectiveness and interpretability in text classification tasks.

3. TF-IDF Vectorization:

   • Transforms text data into numerical features using TF-IDF, capturing the importance of words in the context of the entire dataset.

4. High Accuracy:

   • Achieves a high accuracy rate of 90%, making it reliable for various applications.

5. Versatile Applications:

   • Easily adaptable for various text-based applications, including social media monitoring, customer feedback analysis, and sentiment tracking.