Improving tweet clustering using bigrams formed from word associations

In this work we propose an innovative clustering algorithm for twitter data. In the the context of e-commerce, we use Apiori algorithm to form 2-gram association rules and cluster tweets using self organizing maps. Since tweets are relatively small, word association becomes all the more important in mining the information. To check if 2-grams formed using word associations, help in increasing clustering tendency we use Hopkins index. Tested on two separate datasets, of 200 and 10,000 tweets each related to the key word "Amazon", our results of the analysis show that there is improvement in the clustering tendency in both the datasets. This improvement in clustering tendency is potentially useful because customer grouping based on the tweets can help businesses determine new trends and identify customers with different sentiments.