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  1. Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing (UnstructureNLP '13)
  2. Says who?: automatic text-based content analysis of television news
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Big data opportunities and challenges for IR, text mining and NLP
Mining semantics for culturomics: towards a knowledge-based approach
Information fusion in taxonomic descriptions
Analyzing future communities in growing citation networks
Review rating prediction based on the content and weighting strong social relation of reviewers
Exploiting topic tracking in real-time tweet streams
Sentiment analysis of sentences with modalities
Are words enough?: a study on text-based representations and retrieval models for linking pins to online shops
Says who?: automatic text-based content analysis of television news
Mirroring the real world in social media: twitter, geolocation, and sentiment analysis

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Article

Says who?: automatic text-based content analysis of television news

Content Provider ACM Digital Library
Author De Francisci Morales, Gianmarco Khan, Nasir Mendoza, Marcelo Castillo, Carlos
Abstract We perform an automatic analysis of television news programs, based on the closed captions that accompany them. Specifically, we collect all the news broadcasted in over 140 television channels in the US during a period of six months. We start by segmenting, processing, and annotating the closed captions automatically. Next, we focus on the analysis of their linguistic style and on mentions of people using NLP methods. We present a series of key insights about news providers, people in the news, and we discuss the biases that can be uncovered by automatic means. These insights are contrasted by looking at the data from multiple points of view, including qualitative assessment.
Starting Page 53
Ending Page 60
Page Count 8
File Format PDF
ISBN 9781450324151
DOI 10.1145/2513549.2513558
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-10-28
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Closed captions News framing
Content Type Text
Resource Type Article
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