Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web

Abel, Fabian; Gao, Qi; Houben, Geert‐Jan; Tao, Ke

doi:10.1007/978-3-642-21064-8_26

Cited by 194 publications

(149 citation statements)

References 8 publications

Supporting

Mentioning

145

Contrasting

Unclassified

Order By: Relevance

“…In particular, profiles are enriched with entities and topics extracted from news articles that are linked with Twitter messages. In previous work, we have done the study on selecting appropriate news articles for enriching tweets [12]. We revealed that for Twitter messages, which do not explicitly link to external Web resources we can find related news articles that report about the same event as the Twitter message with a high precision of more than 70%.…”

Section: Definition 1 (User Profile)mentioning

confidence: 99%

“…Rowe et al [11] proposed to exploit contextual information to enrich the semantics of Twitter messages. In previous work, we followed this suggestion and linked tweets to related Web resources [12]. Given this semantic enrichment, we proposed user modeling strategies that allow for recommending news articles.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, they propose to map tweets to events and exploit semantic descriptions of these events to clarify the semantic meaning of tweets. In previous work, we proposed strategies to automatically map Twitter messages to related news articles which allows us to exploit the news articles to enrich the semantics of individual tweets [12]. This enrichment builds the basis for the user modeling strategies [13] that are made available via the TUMS service which we present in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

TUMS: Twitter-Based User Modeling Service

Tao

Abel

Gao

et al. 2012

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Twitter is today's most popular micro-blogging service on the Social Web. As people discuss various fresh topics, Twitter messages (tweets) can tell much about the current interests and concerns of a user. In this paper, we introduce TUMS, a Twitter-based User Modeling Service, that infers semantic user profiles from the messages people post on Twitter. It features topic detection and entity extraction for tweets and allows for further enrichment by linking tweets to news articles that describe the context of the tweets. TUMS is made publicly available as a Web application. It allows end-users to overview Twitter-based profiles in a structured way and allows them to see in which topics or entities a user was interested in a specific point in time. Furthermore, it provides Twitter-based user profiles in RDF format and allows applications to incorporate these profiles in order to adapt their functionality to the current interests of a user. TUMS is available via: http://wis.ewi.tudelft.nl/tums/

show abstract

Section: Definition 1 (User Profile)mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

TUMS: Twitter-Based User Modeling Service

Tao

Abel

Gao

et al. 2012

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [8] it had mentioned that usually Web sites allow interchange of information and let analyze collective behavior, because is hard analyze user individual information. That way is important the fact that users need to be linked in social networks, and it happened because they have some interests or characteristics in common.…”

Section: Frameworkmentioning

confidence: 99%

Extraction and Classification of Twitter Messages to Apply in Business Intelligence

Carlos¹,

Paola²

2013

LNSE

View full text Add to dashboard Cite

Abstract-Nowadays the participation of users in information networks is increasing. Every day millions of people share opinions, experiences, modes through a comment, etc. However, this source of information called collective intelligence is unutilized at it is maximum capacity, because there comes a point where the user is oversaturated with so much information. The reason would be advisable to have the means to exploit the information that is expressed through the comments so that is synthesized and allow support to collective needs. This paper presents a way to exploit the information found within tweets of Twitter through programming language, ie applying data mining to the comments made. The aim is to show that through this language is simple start analyzing what the users say. The business intelligence system through social networks is proposed such as a case of study, which shows how they can be used in comments to support making decision. The Social Web has had a drive through the various websites that allow you to share information and which are known as social networking. The participation of users across these networks is astonishing; actually there are millions of people around the world who participates daily with at least one comment. But considering that a simple comment may contain relevant information, and joined the thousands of comments at a given time there is a source of collective information, synthesize that information would be important to support collective needs. This source of information can be manipulated in order to find patterns in the words that are used in the comments, and use them as benefits for the users.Index Terms-Information networks, collective intelligence, twitter, business intelligence.

show abstract

“…As will be commented in the next section, many researchers have focused on the analysis of hashtags in order to perform complex knowledge-based tasks on a corpus of tweets [12,13]. However, most of the scientific contributions have focused almost exclusively on a purely syntactic analysis of the content of tweets, including their hashtags.…”

Section: Introductionmentioning

confidence: 99%