Leveraging the Semantics of Tweets for Adaptive Faceted Search on Twitter

Abel, Fabian; Celik, Ilknur; Houben, Geert‐Jan; Siehndel, Patrick

doi:10.1007/978-3-642-25073-6_1

Cited by 55 publications

(54 citation statements)

References 13 publications

(12 reference statements)

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“…(v) What semantic-based information access methods can help address the complex information seeking behavior in social media? For example, semantic search in social media [71] and social media streams recommendation [72].…”

Section: Using Lod To Interpret Unstructured Datamentioning

confidence: 99%

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Ristoski

Paulheim

2016

SSRN Journal

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a b s t r a c tData Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. This survey article gives a comprehensive overview of those approaches in different stages of the knowledge discovery process. As an example, we show how Linked Open Data can be used at various stages for building content-based recommender systems. The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.

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Section: Using Lod To Interpret Unstructured Datamentioning

confidence: 99%

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Ristoski

Paulheim

2016

SSRN Journal

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“…The relevance of metadata data is highlighted in [8]. [9,10] discuss the relevance of metadata and its applications. Extracting facets from the tweets in order to facilitate multi-faceted search was explored [9].…”

Section: Related Workmentioning

confidence: 99%

“…[9,10] discuss the relevance of metadata and its applications. Extracting facets from the tweets in order to facilitate multi-faceted search was explored [9]. The entities identified were tagged to facilitate search.…”

Section: Related Workmentioning

confidence: 99%

Automating Corpora Generation with Semantic Cleaning and Tagging of Tweets for Multi-dimensional Social Media Analytics

Javed¹,

L²

2015

IJCA

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Developing corpora from social media content involves convoluted cleaning. In this paper we propose and implement the automation of corpora building for facilitating social media mining and analytics. This automation process incorporates: a) metadata extraction and structuring b) semantic cleaning with tagging and c) learning domain terms/entities. The implementation performs comprehensive cleaning including abbreviation and slang correction, phonetic matching using metaphone algorithm, splitting joined words and identifying/learning entities. It identifies the entities, tags them and creates/updates a knowledgebase (KB) comprising of domain terms. The corpus thus constructed, facilitates multidimensional analysis and summarization. This proposed technique was tested with an experiment in which real world streaming tweets pertaining to Indian politics were collected, structured, cleaned and tagged. The results of the automation experiment can be stated as follows: a) the tweets although primarily in English, contained at times words from the regional languages. The algorithm does not recognize these words and they are construed as domain terms. An accuracy of 85.55% was achieved in identifying the correct domain terms and entities. b) The automation required human feedback and intervention which progressively reduced and reached a figure of 18% with the update and enhancement of the KB. This paper assumes relevance because the implementation automates the entire process of collecting and cleaning the tweets and yields a corpus suitable for multifaceted analysis.

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“…"Dr","Mr") or post-posed modifiers, that belongs to a class of the NEEL Taxonomy (Appendix A) and is linked to a DBpedia resource. Semantically enriched tweets have been shown to help addressing complex information seeking behaviour in social media, such as semantic search [1], deriving user interests [2], and disaster detection [77].…”

Section: Introductionmentioning

confidence: 99%

“…More than three hundred teams have explicitly acquired the corpora in the four years, underlining the importance of the challenges in the field. 1 The NEEL challenges have also attracted from strong interest from the industry as both participants and funding agencies. For example, in 2013 and 2015 the best performing systems were proposed by industrial participants.…”

Section: Introductionmentioning

confidence: 99%

Lessons learnt from the Named Entity rEcognition and Linking (NEEL) challenge series

Rizzo

Pereira

Varga³

et al. 2017

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Abstract. The large number of tweets generated daily is providing decision makers with means to obtain insights into recent events around the globe in near real-time. The main barrier for extracting such insights is the impossibility of manual inspection of a diverse and dynamic amount of information. This problem has attracted the attention of industry and research communities, resulting in algorithms for the automatic extraction of semantics in tweets and linking them to machine readable resources. While a tweet is shallowly comparable to any other textual content, it hides a complex and challenging structure that requires domainspecific computational approaches for mining semantics from it. The NEEL challenge series, established in 2013, has contributed to the collection of emerging trends in the field and definition of standardised benchmark corpora for entity recognition and linking in tweets, ensuring high quality labelled data that facilitates comparisons between different approaches. This article reports the findings and lessons learnt through an analysis of specific characteristics of the created corpora, limitations, lessons learnt from the different participants and pointers for furthering the field of entity recognition and linking in tweets.

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Leveraging the Semantics of Tweets for Adaptive Faceted Search on Twitter

Cited by 55 publications

References 13 publications

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Automating Corpora Generation with Semantic Cleaning and Tagging of Tweets for Multi-dimensional Social Media Analytics

Lessons learnt from the Named Entity rEcognition and Linking (NEEL) challenge series

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