Sentiment analysis of social media content using N-gram graphs

Aisopos, F; Papadakis, G; Varvarigou, T

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	https://dspace.lib.ntua.gr/xmlui/handle/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