Streaming trend detection in Twitter

Benhardus, James; Kalita, Jugal

doi:10.1504/ijwbc.2013.051298

Cited by 132 publications

(89 citation statements)

References 17 publications

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“…The algorithm calculates the most frequent words that appear within the hashtag and maps it to the 69 Linguistic Inquiry and 3 https://blog.twitter.com/2013/behind-the-numbers-tweets-per-minute Word Count (LIWC) 4 . If the most frequent word is positive, it means the talk regarding the topic is positive.…”

Section: The Challenge Of Mining Stream Datamentioning

confidence: 99%

“…Wang and Lee [64], from HP Labs, demonstrate that trending topics can be performing statistical computing on the words present in the posts [39,24]. Benhardus and Kalita develop an algorithm that identifies trending topics by computing term frequencies and the inverse document frequency [4]. They start with normalized term frequency within document d j as shown in this equation:…”

Section: Current Approachesmentioning

confidence: 99%

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Classifying Short Text in Social Media: Twitter as Case Study

Kateb¹,

Kalita²

2015

IJCA

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With the huge growth of social media, especially with 500 million Twitter messages being posted per day, analyzing these messages has caught intense interest of researchers. Topics of interest include micro-blog summarization, breaking news detection, opinion mining and discovering trending topics. In information extraction, researchers face challenges in applying data mining techniques due to the short length of tweets as opposed to normal text with longer length documents. Short messages lead to less accurate results. This has motivated investigation of efficient algorithms to overcome problems that arise due to the short and often informal text of tweets. Another challenge that researchers face is stream data, which refers to the huge and dynamic flow of text generated continuously from social media. In this paper, we discuss the possibility of implementing successful solutions that can be used to overcome the inconclusiveness of short texts. In addition, we discuss methods that overcome stream data problems.

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Section: The Challenge Of Mining Stream Datamentioning

confidence: 99%

Section: Current Approachesmentioning

confidence: 99%

Classifying Short Text in Social Media: Twitter as Case Study

Kateb¹,

Kalita²

2015

IJCA

Self Cite

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“…Broadly, there are 2 types of nodes that can exist in a Storm Topology: 1) Spouts These are nodes that create an input stream of data for the Topology either by generating it randomly on the fly, or by connecting to a third party source of events through a streaming API (for example, the Twitter streaming API [13]). The Spout collects the events from the source and emits them to the rest of the Topology.…”

Section: Abstract Syntaxmentioning

confidence: 99%

Stormgen - A Domain specific language to create ad-hoc Storm Topologies

Santurkar¹,

Arora²,

Chandrasekaran³

2014

Annals of Computer Science and Information Systems

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Abstract-Large-scale distributed data processing has gained significant momentum in research in the past decade. With the introduction of MapReduce, many frameworks have been developed that either implement MapReduce or provide additional functionalities useful in a larger domain. While the framework introduced in the MapReduce paper performs batch-processing of data, Apache Storm performs real-time computation on data. Storm does this with the help of Topologies, and the constituents of the Topology are developed using General-purpose Programming Languages (GPL). A Domain-specific Language (DSL) can provide a higher level of abstraction over GPLs and model the specialized features of a particular domain in a better way. In this paper, we propose the development of Storm Topology generator (Stormgen), a DSL for Storm Topology development, and show how the specifications of this DSL can be utilized during the code generation of exact Storm Topology components in Java. The parser and code generator for Stormgen's syntax are developed using the Eclipse Modelling Framework. The practical use of Stormgen is illustrated with a case study which considers the modelling of a Topology for the Word Count application.

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“…The authors of TwitterMonitor [8] present a system to automatically extract trends in the stream of tweets. A quite similar approach is proposed in [9]. However, to the best of our knowledge, most existing studies mainly focus on a specific analysis of tweets and do not provide general tools for the decision maker (i.e., for manipulating the information embedded in tweets according to their needs).…”

Section: Related Workmentioning

confidence: 99%

“…The use of hierarchies to manage words in the tweets enables us to offer a contextualization in order to better understand the content. We illustrate our proposal by using the MeSH 8 (Medical Subject Headings) which is used for indexing PubMed articles 9 . The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Towards an On-Line Analysis of Tweets Processing

Bringay

Béchet

Bouillot

et al. 2011

Lecture Notes in Computer Science

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Abstract. Tweets exchanged over the Internet represent an important source of information, even if their characteristics make them difficult to analyze (a maximum of 140 characters, etc.). In this paper, we define a data warehouse model to analyze large volumes of tweets by proposing measures relevant in the context of knowledge discovery. The use of data warehouses as a tool for the storage and analysis of textual documents is not new but current measures are not well-suited to the specificities of the manipulated data. We also propose a new way for extracting the context of a concept in a hierarchy. Experiments carried out on real data underline the relevance of our proposal.

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Streaming trend detection in Twitter

Cited by 132 publications

References 17 publications

Classifying Short Text in Social Media: Twitter as Case Study

Classifying Short Text in Social Media: Twitter as Case Study

Stormgen - A Domain specific language to create ad-hoc Storm Topologies

Towards an On-Line Analysis of Tweets Processing

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