Sentiment Analysis Project

Overview This sentiment analysis project is based on a logistic regression model and is designed to detect three sentiments: positive, negative, and neutral. The model is trained on a labeled dataset to analyze textual data and predict the sentiment associated with each piece of text.

Features Logistic Regression Model: The core of the sentiment analysis is a logistic regression model. Logistic regression is employed for its simplicity and effectiveness in binary and multiclass classification tasks.

Multiclass Sentiment Detection: The model is trained to classify text into three sentiment classes: positive, negative, and neutral. This allows for a more nuanced understanding of sentiment in textual data.

Text Preprocessing: Prior to model training, the text data undergoes preprocessing steps, including tokenization, removal of stop words, and stemming. These steps contribute to enhancing the model's ability to recognize sentiment.

Input: Enter the text for sentiment analysis when prompted. Output: The model will output the predicted sentiment: positive, negative, or neutral.

Dataset The model is trained on a labeled dataset consisting of various texts with associated sentiment labels. The dataset used for training is not included in this repository, but you can use Kaggle for Sentiment140 dataset with 1.6 million tweets.

Acknowledgments The logistic regression model used in this project is adapted from the scikit-learn library. The dataset used for training is sourced from Kaggle and named training.1600000.processed.noemoticon.csv.

Contact For inquiries or suggestions, please contact [swatikhanduri2404@gmail.com].