Short Text Classification: A Survey

Song, Ge; Ye, Yunming; Du, Xiaolin; Huang, Xiaohui; Bie, Shifu

doi:10.4304/jmm.9.5.635-643

Cited by 134 publications

(76 citation statements)

References 14 publications

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“…There are many research found that applied text mining in SMS spam classification [11] [28]. However, application of text mining for spam messages is not limited to SMS but also include for email, webpage, and social media platform.…”

Section: B the Significance Of Text Pre-processingmentioning

confidence: 99%

“…In the previous research, there are many studies found for classifying and differentiate messages between legit or ham and spam [9][10] [11], but no publication for measuring the possible harm that this threat could convey, especially with the employment of AIS. With the intention to step ahead, this paper will articulate the design and development of a prototype in conducting and implementing a severity assessment for a text spam message.…”

Section: Introductionmentioning

confidence: 99%

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The Design and Development of Spam Risk Assessment Prototype: In Silico of Danger Theory Variants

Zainal¹,

Jali²

2017

ijacsa

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Abstract-Now-a-days, data is flowing with various types of information and it is absolutely enormous and moreover, it is in unstructured form. These raw data is meaningless unless it is processed and analyzed to retrieve all the valuable and meaningful information. In this paper, a design and principal functionalities of the system prototype is introduced. A process of information retrieval by applying the text mining with Artificial Immune System (AIS) is proposed to discover the possible level of severity for a Short Messaging Service (SMS) spam. This is expected to be a potential tool in retrieving an implicit danger that a spam might impact to the recipients. Furthermore, the development of this tool can be considered as an emergence of another data mining tool that also exceedingly possible to be embedded with another existing tool.

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Section: B the Significance Of Text Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Design and Development of Spam Risk Assessment Prototype: In Silico of Danger Theory Variants

Zainal¹,

Jali²

2017

ijacsa

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“…Text classification is commonly defined simply as follow ( Fig.1) [22]: Given a set of documents D and a set of categories (or labels) C, we define a function F that will assign a value from the set of C to each document in D. In this task, we have a training set with labels and a test set without labels. Our goal is to decide the category of each document in test set by training the function F with training set's features.…”

Section: Related Workmentioning

confidence: 99%

Short Text Classification Based on Latent Topic Modeling and Word Embedding

He¹,

Ma²

2017

dtcse

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Abstract.With the rapid development of the social network and e-commerce, we are exposed to enormous short text every day, ranging from twitters, movie comments, search snippets to news summaries. To classify the short and sparse text accurately is always the basic need for us to deal with information efficiently. However, previous methods fail to achieve high performance due to the sparseness and meaningless of the representation of text. The key breakout lies on the appropriate representation of the words, on which we excogitate a new framework. By discovering the latent topics in the related data crawled from the web, topic distribution can describe the text content in general. Combining with the word embedding generated from the online universal data, the proposed method is a more dense representation, containing semantic information from two different aspects. With this semantic representation of the texts, this framework greatly outperform the previous methods even using the most common SVM classifier, improving the accuracy by 11.58% on standard data set.

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“…Similarly, in an event detection task in Twitter, tweets being posted on a continuous basis need to be analysed and classified in order to detect the occurrence of some event. Nevertheless, traditional sequence classification approaches (Song et al, 2014;Gorrell and Bontcheva, 2016) ignore the time information in these textual data sequences. In this paper, we aim to consider the continuous time information along with the textual information for classifying sequences of temporal textual data.…”

Section: Introductionmentioning

confidence: 99%

Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter

Łukasik¹,

Srijith²,

Vu³

et al. 2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

113

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Classification of temporal textual data sequences is a common task in various domains such as social media and the Web. In this paper we propose to use Hawkes Processes for classifying sequences of temporal textual data, which exploit both temporal and textual information. Our experiments on rumour stance classification on four Twitter datasets show the importance of using the temporal information of tweets along with the textual content.

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Short Text Classification: A Survey

Cited by 134 publications

References 14 publications

The Design and Development of Spam Risk Assessment Prototype: In Silico of Danger Theory Variants

The Design and Development of Spam Risk Assessment Prototype: In Silico of Danger Theory Variants

Short Text Classification Based on Latent Topic Modeling and Word Embedding

Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter

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