research data . Dataset . 2017

Sentiment and Topic Analysis

Bartolome, Abigail; Bock, Matthew; Vinayagam, Radha Krishnan; Krishnamurthy, Rahul;
Open Access English
  • Published: 03 May 2017
  • Publisher: Virginia Tech
  • Country: United States
Abstract
The IDEAL (Integrated Digital Event Archiving and Library) and Global Event and Trend Archive Research (GETAR) projects have collected over 1.5 billion tweets, and webpages from social media and the World Wide Web and indexed them to be easily retrieved and analyzed. This gives researchers an extensive library of documents that reflect the interests and sentiments of the public in reaction to an event. By applying topic analysis to collections of tweets, researchers can learn the topics of most interest or concern to the general public. Adding a layer of sentiment analysis to those topics will illustrate how the public felt in relation to the topics that were fo...
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free text keywords: topic analysis, sentiment analysis, tweets, natural language processing, nlp, linguistic analysis
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Digital Humanities and Cultural Heritage
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