ABSTRACT
Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world and is currently overwhelmed by massive amount of tweets generated by its users. To categorize their tweets many users, use hashtag. A hash tag, a keyword prefixed with a hash symbol (#), is a feature in Twitter to organize tweets and facilitate effective search among a massive volume of data. However, hashtag is not restricted in any way in terms of usage, which leads to large number of hashtags in twitter data set. Furthermore, hash tags can be used to collect user opinion on public events, community etc. However, there are very few tweets containing hashtags, which impedes the quality of search results and their further usage in various applications. Therefore, hashtag recommendation has become a particularly important research problem. Existing methods have proposed apersonalized hash tag, content based hash tag and hash tag recommendation using TF-IDF approaches which is quite complex. In this paper we address an automatic hashtag recommendation system which uses simple map reduce functions and rule mining techniques. Map reducing function takes the tweet as input and process the given tweet to give key value pair as output. This Output is fed back to rule mining techniques to generate a frequent itemset and that is recommend as hash tag. Our theoretical approach says that this method recommends a hashtag which is more stable and reliable than other approaches.
