This document summarizes a project that compares three approaches to topic detection in tweets: LDA, KNN, and K-Means clustering. The goal is to understand the tradeoffs between the approaches. The dataset contains over 6,000 tweets on topics like the FA Cup and US elections. All approaches used boosting techniques like duplicating hashtags and proper nouns. The results found that KNN performed best using trigrams, and boosting significantly improved LDA's performance. In conclusion, trigrams provided the most accuracy for topic detection in tweets due to their ability to capture semantic and syntactic similarities, especially for retweets.