Social media based event summarization by user–text–image co-clustering

Qian, Xueming; Li, Mingdi; Ren, Yayun; Jiang, Shuhui

doi:10.1016/j.knosys.2018.10.028

Cited by 37 publications

(11 citation statements)

References 42 publications

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“…The main characteristic of a cluster is its centroid. This algorithm stabilizes or at best completely stops the change in the centroid of the cluster [31], [34], [35]. At first, it selects the initial centroids for multiple documents.…”

Section: F K-means Clusteringmentioning

confidence: 99%

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Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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In natural language processing, text summarization is an important application used to extract desired information by reducing large text. Existing studies use keyword-based algorithms for grouping text, which do not give the documents' actual theme. Our proposed dynamic corpus creation mechanism combines metadata with summarized extracted text. The proposed approach analyzes the mesh of multiple unstructured documents and generates a linked set of multiple weighted nodes by applying multistage Clustering. We have generated adjacency graphs to link the clusters of various collections of documents. This approach comprises of ten steps: pre-processing, making multiple corpuses, first stage clustering, creating sub-corpuses, interlinking sub-corpuses, creating page rank keyword dictionary of each sub-corpus, second stage clustering, path creation among clusters of sub-corpuses, text processing by forward and backward propagation for results generation. The outcome of this technique consists of interlinked subcorpuses through clusters. We have applied our approach to a News dataset, and this interlinked corpus processing follows step by step clustering to search the most relevant parts of the corpus with less cost, time, and improve content detection. We have applied six different metadata processing combinations over multiple text queries to compare results during our experimentation. The comparison results of text satisfaction show that Page-Rank keywords give 38% related text, single-stage Clustering gives 46%, twostage Clustering gives 54%, and the proposed technique gives 67% associated text. Furthermore, this approach covers/searches the relevant data with a range of most to less relevant content. It provides the systematic query-relevant corpus processing mechanism, which automatically selects the most relevant subcorpus through dynamic path selection. We used the SHAP model to evaluate the proposed technique, and our evaluation results proved that the proposed mechanism improved text processing. Moreover, combining text summarization features, shown satisfactory results compared to the summaries generated by general models of abstractive & extractive summarization.

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Section: F K-means Clusteringmentioning

confidence: 99%

“…We identified similar documents by applying the K-Means clustering process. KMC gives optimal results in many situations [17], [34], [46]. We compared the GB reduced text with {Actual-Text, SS-reduced text, PRK} by calculating the CS values.…”

Section: B Selection Of Reduced Corpus To Apply Fscmentioning

confidence: 99%

Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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“…Web crawling is the most common technique to gather numerous online reviews in the text or picture format on social media [24]. Although crawlers can scrape the target dataset conveniently and continuously from any websites, they consume resources of visited systems and will cause the load and schedule issue [25].…”

Section: Social Media Miningmentioning

confidence: 99%

Multifunctional Product Marketing Using Social Media Based on the Variable-Scale Clustering

Wang

Gao

2019

Teh. vjesn.

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Customers' demands have become more dynamic and complicated owing to the functional diversity and lifecycle reduction of products which pushes enterprises to identify the real-time needs of distinct customers in a superior way. Meanwhile, social media turned as an emerging channel where customers often spontaneously can express their perceptions and thoughts about products promptly. This paper examines the customer satisfaction identification and improvement problem based on social media mining. First, we proposed the public opinion sensitivity index (POSI) to uncover target customers from extensive short-textual reviews. Subsequently, we presented a customer segmentation approach based on the sentiment analysis and the variable-scale clustering (VSC). The approach is able to get several customer clusters with the same satisfaction level where customers belonging to each cluster have similar interests. Finally, customer-centered marketing strategies and customer difference marketing campaigns are planned under the shadow of customer segmentation results. The experiments illustrate that our proposed method can support marketing decision marketing in practice that enriches the intention of the current customer relationship management.

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“…For example, in the area of Bioinformatics, many advanced methods [11], [26] have been proposed for the clustering of unstructured biomedical texts so as to construct the domain knowledge graph, assemble DNA sequences, etc. In the area of social media information extraction, many effective methods [17], [28] have been developed for the clustering of (generally short) user posts so as to discover trending events, identify rumors/false information, etc.…”

Section: Related Workmentioning

confidence: 99%

P-Gram: Positional N-Gram for the Clustering of Machine-Generated Messages

et al. 2019

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An IT system generates messages for other systems or users to consume, through direct interaction or as system logs. Automatically identifying the types of these machine-generated messages has many applications, such as intrusion detection and system behavior discovery. Among various heuristic methods for automatically identifying message types, the clustering methods based on keyword extraction have been quite effective. However, these methods still suffer from keyword misidentification problems, i.e., some keyword occurrences are wrongly identified as payload and some strings in the payload are wrongly identified as keyword occurrences, leading to the misidentification of the message types. In this paper, we propose a new machine language processing (MLP) approach, called P-gram, specifically designed for identifying keywords in, and subsequently clustering, machine-generated messages. First, we introduce a novel concept and technique, positional n-gram, for message keywords extraction. By associating the position as meta-data with each n-gram, we can more accurately discern which n-grams are keywords of a message and which n-grams are parts of the payload information. Then, the positional keywords are used as features to cluster the messages, and an entropy-based positional weighting method is devised to measure the importance or weight of the positional keywords to each message. Finally, a general centroid clustering method, K-Medoids, is used to leverage the importance of the keywords and cluster messages into groups reflecting their types. We evaluate our method on a range of machine-generated (text and binary) messages from the real-world systems and show that our method achieves higher accuracy than the current state-ofthe-art tools. INDEX TERMS Machine-generated messages, positional n-gram, clustering.

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Social media based event summarization by user–text–image co-clustering

Cited by 37 publications

References 42 publications

Unstructured Text Documents Summarization With Multi-Stage Clustering

Unstructured Text Documents Summarization With Multi-Stage Clustering

Multifunctional Product Marketing Using Social Media Based on the Variable-Scale Clustering

P-Gram: Positional N-Gram for the Clustering of Machine-Generated Messages

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