27 April 2018 NewsAnalyticalToolkit: an online natural language processing platform to analyze news
Author Affiliations +
In today’s increasingly divided political climate there is a need for a tool that can compare news articles and organizations so that a user can receive a wider range of views and philosophies. NewsAnalyticalToolkit allows a user to compare news sites and their political articles by coverage, mood, sentiment, and objectivity. The user can sort through the news by topic, which was determined using Natural Language Processing (NLP) and Latent Dirichlet Allocation (LDA). LDA is a probabilistic method used to discover latent topics within a series of documents and cluster them accordingly. Each news article can be considered a mix of multiple topics and LDA assigns a set of topics to each with a probability of it pertaining to that topic. For each topic, a user can then discover the coverage, mood, sentiment and objectivity expressed by each author and site. The mood was determined using IBM Watsons ToneAnalyzerV3, which uses linguistic analysis to detect emotional, social and language tones in written text. The analyzer is based on the theory of psycholinguistics, a field of research that explores the relationship between linguistic behavior and psychological theories. The sentiment and objectivity scores were determined using SentiWordNet, which is a lexical database that groups English words into sets of synonyms and assigns sentiment scores to them. The features were combined to plot an interactive graph of how opinionated versus how analytical an article is, so that the user can click through them to get a better understanding of the topic in question.
Conference Presentation
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Ian McCann, Ian McCann, Amirhessam Tahmassebi, Amirhessam Tahmassebi, Simon Y. Foo, Simon Y. Foo, Gordon Erlebacher, Gordon Erlebacher, Anke Meyer-Baese, Anke Meyer-Baese, } "NewsAnalyticalToolkit: an online natural language processing platform to analyze news", Proc. SPIE 10653, Next-Generation Analyst VI, 106530P (27 April 2018); doi: 10.1117/12.2304646; https://doi.org/10.1117/12.2304646


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