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An Enhanced Corpus for Arabic Newspapers Comments
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In: ISSN: 1683-3198 ; International Arab Journal of Information Technology ; https://hal.archives-ouvertes.fr/hal-03124728 ; International Arab Journal of Information Technology, Colleges of Computing and Information Society (CCIS), 2020, 17 (5), pp.789-798. ⟨10.34028/iajit/17/5/12⟩ (2020)
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Application of Support Vector Machine (SVM) in the Sentiment Analysis of Twitter DataSet
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In: Applied Sciences ; Volume 10 ; Issue 3 (2020)
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Furosine and HMF determination in prebiotic-supplemented infant formula from Spanish market
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Análisis de sentimientos a nivel de aspecto usando ontologías y aprendizaje automático ; Aspect-based sentiment analysis using ontologies and machine learning
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Modality prediction of biomedical literature images using multimodal feature representation ... : Klassifikation von Bildern der biomedizinischen Literatur unter Anwendung multimodaler Merkmale ...
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| 9 |
Language identification of multilingual posts from Twitter: a case study
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Continuous Speech Classification Systems for Voice Pathologies Identification
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In: IFIP Advances in Information and Communication Technology ; 6th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS) ; https://hal.inria.fr/hal-01343485 ; 6th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Apr 2015, Costa de Caparica, Portugal. pp.217-224, ⟨10.1007/978-3-319-16766-4_23⟩ (2015)
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Sentiment analysis: text, pre-processing, reader views and cross domains
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A predictive model to detect online cyberbullying
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Abstract:
Cyberbullying is prevalent in most countries across the globe. The aim of this research was to develop a predictive model to identify the occurrence of cyberbullying tweets on Twitter. The paradigm shift in the Internet of Things was observed a decade ago, which resulted in enormous growth in the number of active Internet users. Today, this number has exceeded three billion. Social networking websites are classic examples of Internet applications that have large numbers of active users. Twitter, for instance, is one of the most famous social networking portals, with more than 300 million active users at any given time. However, unfortunately it is also a stage for users who are involved in unethical use of the Internet, such as cyberbullying. With such a staggering number of active users on the Internet, cyberbullying has become a widespread global phenomenon. It has extremely adverse effects on its victims. In some cases victims have committed suicide in response to the shame and hatred that is associated with cyberbullying . In this research, 1313 unique tweets were collected from Twitter. With the help of psychological studies referring to, the behavior of individuals and the use of dialects pertaining to verbal aggressiveness, 376 tweets were manually tagged as cyberbullying tweets in the first phase. In the next phase, every word in a tweet was individually categorised based on the pragmatics of language. In order to achieve this, tweets were categorised using Linguistic Inquiry and Word Count (LIWC), a psychometric evaluation tool that categorises text based on Linguistic Processes, Psychological Processes, Personal Concerns and Spoken Categories. Collectively, they add up to 67 sub-word-categories. In the next step of the psychometric evaluation, LIWC calculated the degree to which different word-categories were used by people in cyberbullying. Psychometric evaluation therefore aided in effective text categorisation and quantifying the degree of word usage, which was observed to be a gap in previous studies. As a result, tweets were converted to a multi-dimensional attribute relational numeric dataset. This dataset was very rich in terms of the information that it carried. This dataset was then used to train machine learning classifiers in Weka to develop a predictive model to detect cyberbullying. The data was randomly segmented 66% for training the predictive model and 34% for testing it. It was seen that the Random Forest classifier built the predictive model with a precision value of 0.97, indicating that binary classifiers outperformed the multiclass classifiers in detecting cyberbullying tweets.
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Keyword:
Cyberbullying; Decision trees; Linguistic Inquiry and Word Count (LIWC); Multilayer perceptron; Natural Language Processing; Pragmatics of language; Predictive analytics; Psychometric analysis; Psychometric evaluation; Random forest; Support vector machines; TAGS archiving tool; Text classification techniques; Tweets; Twitter; Verbal aggression; WEKA
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URL: http://hdl.handle.net/10292/9277
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| 13 |
Analysis of Features for Synthetic Aperture Radar Target Classification
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In: DTIC (2015)
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Virtual sign : a real time bidirectional translator of portuguese sign language
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Modelling Psychological Needs for User-dependent Contextual Suggestion
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In: DTIC (2014)
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Conversion of sign language to Spoken sentences by means of a sensory glove
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The importance of word-final vowel duration for non-native portuguese speaker identification by means of Support Vector Machines
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In: Revista Brasileira de Linguística Aplicada, Vol 14, Iss 3, Pp 689-714 (2014) (2014)
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ReaderBench, o platformă integrată pentru analiza complexității textuale și a strategiilor de lectură
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In: Proc. 10-a Conf. Nat. de Interactiune Om-Calculator (RoCHI 2013) ; https://hal.archives-ouvertes.fr/hal-01412573 ; Proc. 10-a Conf. Nat. de Interactiune Om-Calculator (RoCHI 2013), T. Stefanut; C. Rusu, 2013, Cluj, Romania. pp.39-46 (2013)
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