Sentiment-based and hashtag-based Chinese online bursty event detection

Zou, Xiaomei; Yang, Jing; Zhang, Jianpei

doi:10.1007/s11042-017-5531-y

Cited by 11 publications

(4 citation statements)

References 35 publications

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“…Lexical dictionaries and their limitations in feelings evaluation were addressed in [14,55,56]. In Teodorescu et al [55], each slang expression is manually annotated with meanings assigned to it.…”

Section: Lexicon Dictionary and Ontology-based Solutionsmentioning

confidence: 99%

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A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

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Section: Lexicon Dictionary and Ontology-based Solutionsmentioning

confidence: 99%

“…Teh et al [14] organized hate terms into eight categories. A lexical dictionary that correlates a word with a feeling from the Plutchik's wheel of emotions was used in [56]. In their work, after establishing this correlation, hashtags were evaluated to identify which event the post refers to.…”

Section: Lexicon Dictionary and Ontology-based Solutionsmentioning

confidence: 99%

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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“…Xiaomei et al [57] recently introduced a corpus-based sentiment analysis method for micro-blog streams to detect breaking events. In this model, a classification and burst detection method were used to identify significant events after building a corpus-based dictionary and running sentiment analysis.…”

Section: Sentiment Analysis In Event Detectionmentioning

confidence: 99%

Service quality monitoring in confined spaces through mining Twitter data

Rahimi¹,

Naghizade²,

Stevenson³

et al. 2020

JOSIS

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Promoting public transport depends on adapting effective tools for concurrent monitoring of perceived service quality. Social media feeds, in general, provide an opportunity to ubiquitously look for service quality events, but when applied to confined geographic area such as a transport node, the sparsity of concurrent social media data leads to two major challenges. Both the limited number of social media messages-leading to biased machine-learning-and the capturing of bursty events in the study period considerably reduce the effectiveness of general event detection methods. In contrast to previous work and to face these challenges, this paper presents a hybrid solution based on a novel finetuned BERT language model and aspect-based sentiment analysis. BERT enables extracting aspects from a limited context, where traditional methods such as topic modeling and word embedding fail. Moreover, leveraging aspect-based sentiment analysis improves the sensitivity of event detection. Finally, the efficacy of event detection is further improved by proposing a statistical approach to combine frequency-based and sentiment-based solutions. Experiments on a real-world case study demonstrate that the proposed solution improves the effectiveness of event detection compared to state-of-the-art approaches.

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“…O estudo propõe a categorização dos termos de ódio em 8 categorias distintas. Em (Xiaomei et al, 2018), utiliza-se um dicionário léxico que correlaciona uma palavra com um sentimento da roda de emoções de Plutchik. Após estabelecer essa correlação, hashtags são avaliadas para identificar a que evento se refere a postagem.…”

Section: Soluções Baseadas Em Dicionário Léxico E Em Ontologiasunclassified

Um Estudo Sobre Análise, Representação E Detecção De Intenções De Criminosos Em Postagens Em Mídia Social

Mendonça¹,

Rosa²,

Bonacin³

2019

Atas Da Conferência Ibero-Americana WWW/Internet 2019

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As mídias sociais se transformaram em um instrumento de comunicação entre criminosos, para planejamento e execução de crimes, bem como para recrutar novos membros. Eles utilizam diferentes estratégias de comunicação, tais como linguagens cifradas e gírias restritas a grupos ou facções para burlar investigações. A pesquisa por ferramentas e técnicas para apoiar a atividade investigativa e o processo de prevenção de crimes são de extrema importância; particularmente, a análise e detecção de intenções relacionadas a crimes. Os aspectos tecnológicos, humanos e sociais relacionados a este problema, torna-o um campo rico de estudo, envolvendo a interação entre IHC (Interação Humano-Computador) com diversas áreas de pesquisa. Pesquisas ligadas às áreas de segurança de informação, linguística, aprendizagem de máquina e processamento de linguagem natural, têm contribuído para o avanço na análise e detecção de intenções em mídia social. Este artigo apresenta uma revisão quasi-sistemática da literatura sobre análise, representação e detecção de intenções de criminosos em postagens em mídia social. 27 estudos foram analisados de acordo com as abordagens utilizadas (ex.: técnicas de aprendizagem de máquina), bem como seus fundamentos em aspectos linguísticos, ontologias, semiótica e teoria dos atos da fala. Os resultados apontam avanços na solução do problema e questões de pesquisas em aberto para a área de IHC e relacionadas.

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Sentiment-based and hashtag-based Chinese online bursty event detection

Cited by 11 publications

References 35 publications

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

Service quality monitoring in confined spaces through mining Twitter data

Um Estudo Sobre Análise, Representação E Detecção De Intenções De Criminosos Em Postagens Em Mídia Social

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