Tech Stack

• Python
• Kotlin

Project Description

Developed a sentiment analysis system to analyze a dataset of 25,000 movie reviews using Python and machine learning techniques. The system provides users with a detailed analysis of the sentiment expressed in the reviews.

Features

• Performs sentiment analysis on a dataset of 25,000 movie reviews to classify them as positive or negative.
• Offers users a comprehensive analysis of the sentiment expressed in the reviews.

Implementation Details

• Reads the movie reviews from a dataset and preprocesses them by removing special characters and converting them to lowercase.
• Utilizes TF-IDF vectorization to convert the preprocessed text data into numerical features.
• Trains a machine learning model using the preprocessed and vectorized data for sentiment classification.
• Evaluates the accuracy of the sentiment analysis model on a test set.

Key Achievements

• Successfully developed a sentiment analysis system to analyze a large dataset of 25,000 movie reviews.
• Implemented preprocessing techniques to clean and normalize the text data.
• Utilized TF-IDF vectorization to transform the text data into a numerical representation.
• Trained a machine learning model to accurately classify the sentiment of movie reviews.
• Achieved significant insights into the sentiment expressed in the reviews through extensive analysis.
• Senti - bot , counselling chatbot was developed using kotlin