Current Trends in Text Mining for Social Media

Singh, Tajinder; Kumari, Madhu; Pal, Triveni Lal; Ahsan, Mohammad

doi:10.14257/ijgdc.2017.10.6.02

Cited by 13 publications

(7 citation statements)

References 37 publications

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“…[11], short forms (hlo, hru, luv, etc. ), which make social text noisy and challenging [15,16]. Cluster based approaches for event detection and classification task for online incremental clustering in [17] are used.…”

Section: Related Workmentioning

confidence: 99%

Burst: real-time events burst detection in social text stream

Singh

Kumari

2021

J Supercomput

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Gigantic growth of social media and unbeatable trend of progress in the direction of the web seeking user's interests have generated a storm of social text streams. Seeking information to know the phenomenon of various events in the early stages is quite interesting. Various kinds of social media live streams attract users to participate in real-time events to become a part of an immense crowd. However, the vast amount of text is present on social media, the unnecessary information bogs a social text stream filtering to extract the appropriate topics and events effectively. Therefore, detecting, classifying, and identifying burst events is quite challenging due to the sparse and noisy text of Twitter. The researchers' significant open challenges are the effective cleaning and profound representation of the text stream data. This research article's main contribution is to provide a detailed study and explore bursty event detection in the social text stream. Thus, this work's main motive is to present a concise approach that classifies and detects the event keywords and maintains the record of the event based on related features. These features permit the approach to successfully determine the booming pattern of events scrupulously at different time span. Experiments are conducted and compared with the state-of-the-art methods, which reveals that the proposed approach is proficient to detect valuable patterns of interest and also achieve better scoresto extract burst events on social media posted by various users.

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Section: Related Workmentioning

confidence: 99%

Burst: real-time events burst detection in social text stream

Singh

Kumari

2021

J Supercomput

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“…Word embeddings have been successfully implemented in language models, extractive summarization, machine translation, named entity recognition, disambiguation, parsing, semantic word cloud and social media mining [17].…”

Section: Distributed Approachesmentioning

confidence: 99%

Semantic Representations in Text Data

Pa¹,

Kumari²,

Singh³

et al. 2018

IJGDC

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Automatic text mining processes and other sophisticated natural language processing constructs need realistic representations of text/documents which embed semantics efficiently. All the representations work on the notion that every data contains different explanatory factors (attributes). In this article, we exploit these explanatory factors to study and compare various semantic representation methods for text documents. The article critically reviews recent trends in the area of semi-supervised semantic representations, covering cutting-edge methods in distributed representations such as embeddings. This article gives a broad and synthesized description of various forms of text representations, presented in their chronological order ranging from BoW models to the most recent embeddings learning. Conclusively, various findings taken together provide valuable pointers for researchers looking to work in the field of semantic representations. In addition, the article also shows that one need to develop a model for learning universal embeddings in unsupervised/semi-supervised settings that incorporate contextual as well as word-order information, with language independent features and which would be feasible for large dataset.

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“…Thelwall used social text to detect magnitude, stress and relaxation [24]. Singh et al [25] talked about the trends ongoing in social media. A customizable pipeline focused by Sarker [26] for social media.…”

Section: Introductionmentioning

confidence: 99%

Social crisis detection using Twitter based text mining-a machine learning approach

Rahman

Jahan²,

Sadia³

et al. 2023

Bulletin EEI

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Social-media and blogs are increasingly used for social-communication, an idea and thought publishing platform. Public intentions, wisdom, problems, solutions, mental states are shared in social media. Text is being the best and the most common way to communicate over social networks. All kinds of data shared in social sites like Facebook, Twitter, and Microblogs. People from different pursuance uses these media to publish thoughts and convey messages through text. Consequently, occurrences in social life are rapidly discussed in social blogs in daily manner. This work aims at discovering ongoing social crisis from the Twitter data. Text mining technique and sentiment analysis were applied to detect the current social crisis from the social sites. Twitter data were collected to identify the recent social crisis. Furthermore, the identified crisis was compared to reputed newspapers. A hybrid method used to detect recent social issues resulted nicely. However, our proposed analysis shows identifying rate 89%, 95%, 83%, 53%, and 98% for the top 5 identified crisis accordingly in the date between 27 February and 11 March 2020. The strategy used in this study for the detection of recent social crisis will contribute to the social life and findings of crisis will be eliminated easily.

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Current Trends in Text Mining for Social Media

Abstract: Abstract

Cited by 13 publications

References 37 publications

Burst: real-time events burst detection in social text stream

Burst: real-time events burst detection in social text stream

Semantic Representations in Text Data

Social crisis detection using Twitter based text mining-a machine learning approach

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