Sentiment analysis of social media content using N-gram graphs

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dc.contributor.author Aisopos, F en
dc.contributor.author Papadakis, G en
dc.contributor.author Varvarigou, T en
dc.date.accessioned 2014-03-01T02:53:28Z
dc.date.available 2014-03-01T02:53:28Z
dc.date.issued 2011 en
dc.identifier.uri http://hdl.handle.net/123456789/36338
dc.subject N-gram graphs en
dc.subject Polarity classification en
dc.subject Social media en
dc.subject.other Average Distance en
dc.subject.other Large scale experiments en
dc.subject.other Multiple documents en
dc.subject.other N-gram graphs en
dc.subject.other N-grams en
dc.subject.other Polarity classification en
dc.subject.other Public opinions en
dc.subject.other Real world data en
dc.subject.other Sentiment analysis en
dc.subject.other Social media en
dc.subject.other Technical challenges en
dc.subject.other Textual patterns en
dc.subject.other Data mining en
dc.subject.other Virtual reality en
dc.subject.other Social networking (online) en
dc.title Sentiment analysis of social media content using N-gram graphs en
heal.type conferenceItem en
heal.identifier.primary 10.1145/2072609.2072614 en
heal.identifier.secondary http://dx.doi.org/10.1145/2072609.2072614 en
heal.publicationDate 2011 en
heal.abstract Sentiment Analysis over Social Media facilitates the extraction of useful conclusions about the average public opinion on a variety of topics, but poses serious technical challenges. This is because of the sparse, noisy, multilingual content that is posted on-line by Social Media users. In this paper, we introduce a novel method for capturing textual patterns that inherently supports this challenging type of content. In essence, it creates a graph whose nodes correspond to the character n-grams of a document, while its weighted edges denote the average distance between them. Multiple documents of the same polarity can be aggregated into a polarity class graph, which can be compared with individual documents in order to identify the category of their sentiment. To evaluate our approach, we conducted large scale experiments on a real-world data set stemming from a snapshot of Twitter activity. The outcomes of our evaluation indicate significant improvements over other the methods typically used in this context, not only with respect to effectiveness, but also to efficiency. © 2011 ACM. en
heal.journalName MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops - WSM'11: 3rd ACM Social Media Workshop en
dc.identifier.doi 10.1145/2072609.2072614 en
dc.identifier.spage 9 en
dc.identifier.epage 14 en

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