Clustering of twitter technology tweets and the impact of stopwords on clusters

Year of 2010 could be termed as the year in which Twitter became completely mainstream. Twitter, which started as a means of communicating with friends, became much more than its beginning. Now Twitter is used by companies to promote their new products, used by movie industry to promote movies. A lot of advertising and branding is now tied to Twitter and most importantly any breaking news that happens, the first place one goes and tries to find is to search it on Twitter. Be it the Mumbai attacks that happened in 2008, or the minor earthquakes that happened in Bay Area in 2010 or the twitter revolution cause of the Iran elections, most of the tech and not so tech savvy viewers were following twitter rather than any main stream news channels. In fact most of the breaking news now comes on Twitter because of the huge number of user base rather than the traditional mainstream media. The focus of this paper is clustering with the TF-IDF weighted mechanism of daily technology news tweets of prominent bloggers and news sites using Apache Mahout and to evaluate the effects of introducing and removing stop words on the quality of clustering. This project restricts itself to only tweets in the English language

Enhanced Clustering of Technology Tweets

Ever since Twitter has been widely accepted and has become an immensely popular micro blogging website, it is being used as a primary source of news; be it related to sports, entertainment, politics or technology by several users. It has been proven earlier that the elimination of stop words has a positive impact on the clustering of technology related tweets. The focus of this paper is to enhance the quality of clustering of the technology related Tweets by developing a semi-automated approach to eliminating stop words and by making use of a combination of Canopy and K-means clustering algorithms. The paper also details an algorithmic approach to determine the threshold values for Canopy clustering

Extracting News Events from Microblogs

Twitter stream has become a large source of information for many people, but the magnitude of tweets and the noisy nature of its content have made harvesting the knowledge from Twitter a challenging task for researchers for a long time. Aiming at overcoming some of the main challenges of extracting the hidden information from tweet streams, this work proposes a new approach for real-time detection of news events from the Twitter stream. We divide our approach into three steps. The first step is to use a neural network or deep learning to detect news-relevant tweets from the stream. The second step is to apply a novel streaming data clustering algorithm to the detected news tweets to form news events. The third and final step is to rank the detected events based on the size of the event clusters and growth speed of the tweet frequencies. We evaluate the proposed system on a large, publicly available corpus of annotated news events from Twitter. As part of the evaluation, we compare our approach with a related state-of-the-art solution. Overall, our experiments and user-based evaluation show that our approach on detecting current (real) news events delivers a state-of-the-art performance

Comparison of Clustering Algorithms for the Identification of Topics on Twitter

Topic Identification in Social Networks has become an important task when dealing with event detection, particularly when global communities are affected. In order to attack this problem, text processing techniques and machine learning algorithms have been extensively used. In this paper we compare four clustering algorithms – k-means, k-medoids, DBSCAN and NMF (Non-negative Matrix Factorization) – in order to detect topics related to textual messages obtained from Twitter. The algorithms were applied to a database initially composed by tweets having hashtags related to the recent Nepal earthquake as initial context. Obtained results suggest that the NMF clustering algorithm presents superior results, providing simpler clusters that are also easier to interpret. &nbsp