Model-based Clustering of Short Text Streams

Yin, Jianhua; Chao, Daren; Liu, Zhongkun; Zhang, Wei; Yu, Xiaohui; Wang, Jianyong

doi:10.1145/3219819.3220094

Cited by 65 publications

(52 citation statements)

References 25 publications

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“…LDA has been widely utilized for detecting topics in microposts. Some of these approaches associate a single topic to an individual post [36,37], whereas others consider a document to be a collection of short posts that are aggregated by some criteria like the same author [38], temporal or geographical proximity [39], or content similarity that indicates some relevance (i.e., hashtag or keyword) [40]. Fig 2 shows the top ten words of some LDA topics resulting from tweets that we collected during the 2016 U.S. presidential debates (produced by Twit-terLDA [41]).…”

Section: Related Workmentioning

confidence: 99%

Microblog topic identification using Linked Open Data

Yildirim

Uskudarli

2020

PLoS ONE

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Much valuable information is embedded in social media posts (microposts) which are contributed by a great variety of persons about subjects that of interest to others. The automated utilization of this information is challenging due to the overwhelming quantity of posts and the distributed nature of the information related to subjects across several posts. Numerous approaches have been proposed to detect topics from collections of microposts, where the topics are represented by lists of terms such as words, phrases, or word embeddings. Such topics are used in tasks like classification and recommendations. The interpretation of topics is considered a separate task in such methods, albeit they are becoming increasingly human-interpretable. This work proposes an approach for identifying machineinterpretable topics of collective interest. We define topics as a set of related elements that are associated by having posted in the same contexts. To represent topics, we introduce an ontology specified according to the W3C recommended standards. The elements of the topics are identified via linking entities to resources published on Linked Open Data (LOD). Such representation enables processing topics to provide insights that go beyond what is explicitly expressed in the microposts. The feasibility of the proposed approach is examined by generating topics from more than one million tweets collected from Twitter during various events. The utility of these topics is demonstrated with a variety of topic-related tasks along with a comparison of the effort required to perform the same tasks with words-list-based representations. Manual evaluation of randomly selected 36 sets of topics yielded 81.0% and 93.3% for the precision and F1 scores respectively.

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Section: Related Workmentioning

confidence: 99%

Microblog topic identification using Linked Open Data

Yildirim

Uskudarli

2020

PLoS ONE

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“…The authors stated that precision and recall in those events could be significantly improved when considering denser streams, possibly by using the geolocalisation of texts to better separate synchronous events, though it remains to be evaluated. Yin et al (2018) also aimed at tackling the explosive growth, sparsity and concept drift of short-texts in social media by proposing a stream clustering technique based on the Dirichlet process multinomial mixture model (named MStream). The MStream technique assigns a short-text to either an existing cluster or a new one based on the probabilities computed by the Dirichlet model.…”

Section: Word/character-based Techniquesmentioning

confidence: 99%

“…In response to the mentioned challenges, feature selection techniques should be applied and new learning approaches specifically designed for short-texts should be developed. In this regard, several classification (Yuan et al, 2012;Collins et al, 2015;Cui et al, 2016) and clustering (Yang & Ng, 2009;Ni et al, 2011;Weng & Lee, 2011;Tu & Ding, 2012;Li et al, 2012;Kim et al, 2012;Parikh & Karlapalem, 2013;Wang et al, 2016b;Jia et al, 2018;Yin et al, 2018) techniques included preprocessing steps aiming at reducing the dimensionality of the feature space. In all cases, the applied pre-processing included traditional techniques applied to long-texts.…”

Section: Curse Of Dimensionalitymentioning

confidence: 99%

“…Some of the reviewed classification (Sedhai & Sun, 2015;Li et al, 2017Li et al, , 2018 and clustering (Yang & Ng, 2009;Carullo et al, 2009;Miller et al, 2013;Zhang et al, 2014;Popovici et al, 2014;Liang et al, 2016;Wang et al, 2016b;Yin et al, 2018) techniques proposed to update the learned model as new instances are known, as a means to keep up with the new trends that might appear. In the other cases, the authors did not propose any mechanism to update the models.…”

Section: Curse Of Dimensionalitymentioning

confidence: 99%

“…Given that trends do not only appear, but can also disappear, it might be important to not only add new instances, but also discard old ones. In this regard, only one of the reviewed techniques (Yin et al, 2018) studied the effect of removing old instances, and concluded that it allowed to improve not only the quality of clusters, but also the spacial and computational efficiency of the technique.…”

Section: Curse Of Dimensionalitymentioning

confidence: 99%

See 2 more Smart Citations

Short-text learning in social media: a review

Tommasel

Godoy

2019

The Knowledge Engineering Review

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Social networks occupy a ubiquitous and pervasive place in the life of their users. The substantial amount of content generated and shared by social networking users offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, psychology, information and computer sciences, or education. This situation, in combination with the continuous growth of social media data, creates an imperative need for content organisation. Thus, large-scale text learning tasks in social environments arise as one of the most relevant problems in machine learning and data mining. Interestingly, social media data pose several challenges due to its sparse, high-dimensional and large-volume characteristics. This survey reviews the field of social media data learning, focusing on classification and clustering techniques, as they are two of the most frequent learning tasks. It reviews not only new techniques that have been developed to tackle the new challenges posed by short-texts, but also how traditional techniques can be adapted to overcome such challenges. Then, open issues and research opportunities for social media data learning are discussed.

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Recent methods on short text stream clustering: A survey study

Maden

Karagöz

2023

WIREs Computational Stats

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The volume and the velocity of data in social media are increasing and the social media has become a very useful environment to detect and track the real‐world events. However, to fulfill this, it is crucial to group‐related texts according to their topics and clustering takes an essential role at this point since we have no prior knowledge about the topics and their evolution in social media. In this survey, we review the current approaches and techniques proposed for short text stream clustering in recent years. The reviewed techniques are grouped according to their methodology and discussed in detail. Also, the datasets utilized to evaluate the performance of the proposed methods and the results are summarized together with the clustering quality measures used for these evaluations. Furthermore, current challenges about short‐text stream clustering are discussed.This article is categorized under: Data: Types and Structure > Streaming Data

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Model-based Clustering of Short Text Streams

Cited by 65 publications

References 25 publications

Microblog topic identification using Linked Open Data

Microblog topic identification using Linked Open Data

Short-text learning in social media: a review

Recent methods on short text stream clustering: A survey study

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