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To deal with the sheer volume of information and gain competitive advantage, the news industry has started to explore and invest in news automation. In this paper, we present Reuters Tracer, a system that automates end-to-end news production using Twitter data. It is capable of detecting, classifying, annotating, and disseminating news in real time for Reuters journalists without manual intervention. In contrast to other similar systems, Tracer is topic and domain agnostic. It has a bottom-up approach to news detection, and does not rely on a predefined set of sources or subjects. Instead, it identifies emerging conversations from 12+ million tweets per day and selects those that are news-like. Then, it contextualizes each story by adding a summary and a topic to it, estimating its newsworthiness, veracity, novelty, and scope, and geotags it. Designing algorithms to generate news that meets the standards of Reuters journalists in accuracy and timeliness is quite challenging. But Tracer is able to achieve competitive precision, recall, timeliness, and veracity on news detection and delivery. In this paper, we reveal our key algorithm designs and evaluations that helped us achieve this goal, and lessons learned along the way.

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Purpose. Sentiment analysis and emotion processing are attracting increasing interest in many fields. Computer and information scientists are developing automated methods for sentiment analysis of online text. Most of the research have focused on identifying sentiment polarity or orientation-whether a document, usually product or movie review, carries a positive or negative sentiment. It is time for researchers to address more sophisticated kinds of sentiment analysis. This paper evaluates a particular linguistic framework called appraisal theory for adoption in manual as well as automatic sentiment analysis of news text. Methodology. The appraisal theory is applied to the analysis of a sample of political news articles reporting on Iraq and economic policies of George W. Bush and Mahmoud Ahmadinejad to assess its utility and to identify challenges in adopting this framework. Findings. The framework was useful in uncovering various aspects of sentiment that should be useful to researchers such as the appraisers and object of appraisal, bias of the appraisers and the author, type of attitude and manner of expressing the sentiment. Problems encountered include difficulty in identifying appraisal phrases and attitude categories because of the subtlety of expression in political news articles, lack of treatment of tense and timeframe, lack of a typology of emotions, and need to identify different types of behaviors (political, verbal and material actions) that reflect sentiment. Value. The study has identified future directions for research in automated sentiment analysis as well as sentiment analysis of online news text. It has also demonstrated how sentiment analysis of news text can be carried out.

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