NDLI: Sentiment Crawling: Extremist Content Collection through a Sentiment Analysis Guided Web-Crawler

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Sentiment Crawling: Extremist Content Collection through a Sentiment Analysis Guided Web-Crawler

Content Provider	ACM Digital Library
Author	Mei, Joseph Frank, Richard
Abstract	As the data generated on the internet exponentially increases, developing guided data collection methods become more and more essential to the research process. This paper proposes an approach to building a self-guiding web-crawler to collect data specifically from extremist websites. The guidance component of the web-crawler is achieved through the use of sentiment-based classification rules which allow the crawler to make decisions on the content of the webpage it downloads. First, content from 2,500 webpages was collected for each of the four different sentiment-based classes: pro-extremist websites, anti-extremist websites, neutral news sites discussing extremism and finally sites with no discussion of extremism. Then parts of speech tagging was used to find the most frequent keywords in these pages. Utilizing sentiment software in conjunction with classification software a decision tree that could effectively discern which class a particular page would fall into was generated. The resulting tree showed an 80% success rate on differentiating between the four classes and a 92% success rate at classifying specifically extremist pages. This decision tree was then applied to a randomly selected sample of pages for each class. The results from the secondary test showed similar results to the primary test and hold promise for future studies using this framework.
Starting Page	1024
Ending Page	1027
Page Count	4
File Format	PDF
ISBN	9781450338547
DOI	10.1145/2808797.2809373
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sentiment analysis Extremism Classification Web-crawling
Content Type	Text
Resource Type	Article

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