This document describes a sentiment analysis project that aims to analyze sentiment in tweets about electronic products using data mining techniques. The proposed system uses Naive Bayes and Support Vector Machine (SVM) algorithms to classify tweets as positive, negative, or neutral. SVM achieved higher accuracy rates than Naive Bayes. The system collects tweet data, preprocesses it by removing URLs, symbols, and more. It then trains and tests the algorithms on the preprocessed data and outputs sentiment results as a pie chart and word cloud.