Learning from Twitter Hashtags: Leveraging Proximate Tags to Enhance Graph-Based Keyphrase Extraction

In the micro-blogging service Twitter, the sparseness of text messages is an enormous obstacle in extracting key phrases from tweets. However, regardless of the sparseness in text, tweets include an abundant number of links in the form of hash tags. This paper investigates the possibility of leveraging hash tags in tweets to enhance the graph-based key phrase extraction. By using an auxiliary set of tweets found in hash tags, we show that we can improve extracting key phrases from tweets by augmenting the graph with a wider knowledge context. Specifically, we propose two different approaches for choosing the best hash tags links to use for enhancing graph-based key phrase extraction by either using a frequency approach or a hybrid approach that uses multiple methods for cleverly choosing the best hash tags. Experiments on the proposed approaches showed an improvement in the range of 9% to 37% over the case of ignoring the hash tag links.