Dataset Open Access

News Title Sentiment Dataset

Chang Wei Tan; Christoph Bergmeir; Francois Petitjean; Geoffrey I Webb

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Chang Wei Tan</dc:creator>
  <dc:creator>Christoph Bergmeir</dc:creator>
  <dc:creator>Francois Petitjean</dc:creator>
  <dc:creator>Geoffrey I Webb</dc:creator>
  <dc:description>This dataset is part of the Monash, UEA &amp; UCR time series regression repository.

The goal of this dataset is to predict sentiment score for news title. This dataset contains 83164 time series obtained from the News Popularity in Multiple Social Media Platforms dataset from the UCI repository. This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation. The time series has 3 dimensions. 

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Citation request
Nuno Moniz and Luis Torgo (2018), Multi-Source Social Feedback of Online News Feeds, CoRR

  <dc:subject>time series</dc:subject>
  <dc:title>News Title Sentiment Dataset</dc:title>
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