Twitter is one of the most popular microblogging platforms where users can interact with each other by posting texts of up to 140 characters called tweets. Because of the large and fast growing number of tweets being generated daily, tweet analytics is viewed as one of the fundamental problems of Big data stream. Recently, hashtags, hyperlinked words, in tweets have been applied for tweet retrieval, trend/event detection and advertisement. However, using hashtags for tweet classification remains challenging because we have to cope with context dependent words, slangs, abbreviations, and emoticons with a limited small number of words and an evolving use of hashtags. Most existing approaches deal with classifying tweet sentiments by using the lexicon and meaning of hashtags. Our research aims to classify tweets by topics. Unlike sentiment analytics, hashtags for describing a topic need to be more diverse to cover various aspects of a topic. This paper presents a tweet analytics approach that uses domain-specific knowledge to create a set of strong hashtag predictors for tweet topic classification. The paper describes the approach and preliminary experiments that show promising results toward Big data tweet analytics.