Storyline extraction from news streams aims to extract events under a certain news topic and reveal how those events evolve over time. It requires algorithms capable of accurately extracting events from news articles published in different time periods and linking these extracted events into coherent stories. The two tasks are often solved separately, which might suffer from the problem of erro...

In this paper, we present experimental results on document clustering and classification achieved on the Arabic NEWSWIRE corpus using statistical methods. Arabic is a highly inflecting language. The methods presented here show to be very robust and reliable without morphological analysis.

The emergence of web 2.0 applications has greatly contributed to the increase in volume of information available online today. User generated content can help organizations realize the demands of the public be it in e-commerce, politics or newsrooms. Sentiment analysis plays a pivotal role in the mining of such information thus it is a crucial tool not only in organizations’ decision making pro...

Reading of news articles can trigger emotional reactions from its readers. But comparing to other genre of text, news articles that are mainly used to report events, lack emotion linked words and other features for emotion classification. In this paper, we propose an event anchor based method for emotion classification for news articles. Firstly, we build an emotion linked news corpus through c...

We investigated the pairing of a financial news article prediction system, AZFinText, with sentiment analysis techniques. From our comparisons we found that news articles of a subjective nature were easier to predict in both price direction (59.0% vs 50.4% without sentiment) and through a simple trading engine (3.30% return vs 2.41% without sentiment). Looking into sentiment further, we found t...