Website Crawler & Sentiment Analysis

Back to the University Ranking of my designed application. Ranking technology in my application is to parse tweets crawled from Twitter and then rank related tweets according to their relevance to a specific university. I want to filter high-related tweets (topK) to do the Sentiment Analysis, which will avoid trivial tweets that make our results inaccurate. There are may ranking methods actually, such as rank them based on TF-IDF similarity, text summarization, spatial and temporal factors or machine learning ranking method. Even Twitter itself has provided a method based on time or popularity. However, we need a more advanced method which can filter out the most spam and trivial tweets.