Cyberbullying Detection by Using Artificial Neural Network Models

Bozyiğit, Alican; Utku, Semih; Nasiboğlu, Efendi

doi:10.1109/ubmk.2019.8907118

Cited by 23 publications

(13 citation statements)

References 15 publications

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“…Data set is the most important parameter to test the performance of the proposed model in scientific studies [61]. In this question, we review the properties of input data which feeds proposed ML approaches and generated output of the current studies.…”

Section: Analysis and Resultsmentioning

confidence: 99%

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Systematic literature review of photovoltaic output power forecasting

Başaran

Bozyiğit²,

Siano

et al. 2020

IET Renewable Power Generation

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Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world‐wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works.

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Section: Analysis and Resultsmentioning

confidence: 99%

“…Artificial neural network : ANN is a supervised learning technique which is inspired by the biological human brain that consists of neurons [61]. It includes interconnected multiple layers of nodes with weights and activation functions.…”

Section: Analysis and Resultsmentioning

confidence: 99%

Systematic literature review of photovoltaic output power forecasting

Başaran

Bozyiğit²,

Siano

et al. 2020

IET Renewable Power Generation

View full text Add to dashboard Cite

show abstract

“…On the other hand, their BERT approach was a fine-tuning scheme to obtain the identification of offensive and inoffensive Tweets [61] without mentioned pre-processing tasks. The latest study for Turkish cyberbullying identification was experimented in [32] with the use of tokenization, word n-grams, tf-idf modelling on top of Artificial Neural Network (ANN) and they obtained 91% in terms of F-measure score as their best value. Since we used the same dataset in our BERT evaluations, therefore we choose their best result as baseline in our evaluation.…”

Section: Nlp Problems From Turkish Language Literaturementioning

confidence: 99%

“…This empirical study compares the traditional BoW (or VSM) based ML algorithms and newly proposed BERT in terms of empirical achievement. In order to evaluate performances of the two approaches, we chose diverse six datasets from literature as cyberbullying identification [32], sentiment analysis of Turkish movie and hotel reviews [33], spam Short Message Service (SMS) detection [34], a text classification dataset of six news categories [35] and emotion recognition dataset with six emotion categories [36]. In particular, the mentioned Turkish datasets are studied in the literature and they have morphological language processing steps with corresponding BoW-ML results as baseline.…”

Section: Introductionmentioning

confidence: 99%

Advancing natural language processing (NLP) applications of morphologically rich languages with bidirectional encoder representations from transformers (BERT): an empirical case study for Turkish

et al. 2021

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Language model pre-training architectures have demonstrated to be useful to learn language representations. bidirectional encoder representations from transformers (BERT), a recent deep bidirectional self-attention representation from unlabelled text, has achieved remarkable results in many natural language processing (NLP) tasks with fine-tuning. In this paper, we want to demonstrate the efficiency of BERT for a morphologically rich language, Turkish. Traditionally morphologically difficult languages require dense language pre-processing steps in order to model the data to be suitable for machine learning (ML) algorithms. In particular, tokenization, lemmatization or stemming and feature engineering tasks are needed to obtain an efficient data model to overcome data sparsity or high-dimension problems. In this context, we selected five various Turkish NLP research problems as sentiment analysis, cyberbullying identification, text classification, emotion recognition and spam detection from the literature. We then compared the empirical performance of BERT with the baseline ML algorithms. Finally, we found enhanced results compared to base ML algorithms in the selected NLP problems while eliminating heavy pre-processing tasks.

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“…Using artificial neural network (Bozyigit et al, 2019) on a dataset comprised of 3000 Turkish tweets, F1 score of 91% was obtained. A study ( Özel et al, 2017) comprised of twitter tweets and Instagram post showed that using multinomial Naive Bayes cyberbullying can be classified with an accuracy of 84 % using TF-IDF as features.…”

Section: Related Workmentioning

confidence: 99%

TAC at SemEval-2020 Task 12: Ensembling Approach for Multilingual Offensive Language Identification in Social Media

Anwar¹,

Baig²

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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Usage of offensive language on social media is getting more common these days, and there is a need of a mechanism to detect it and control it. This paper deals with offensive language detection in five different languages; English, Arabic, Danish, Greek and Turkish. We presented an almost similar ensemble pipeline comprised of machine learning and deep learning models for all five languages. Three machine learning and four deep learning models were used in the ensemble. In the OffensEval-2020 competition our model achieved F1-score of 0.85, 0.74, 0.68, 0.81, and 0.9 for Arabic, Turkish, Danish, Greek and English language tasks respectively.

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Cyberbullying Detection by Using Artificial Neural Network Models

Cited by 23 publications

References 15 publications

Systematic literature review of photovoltaic output power forecasting

Systematic literature review of photovoltaic output power forecasting

Advancing natural language processing (NLP) applications of morphologically rich languages with bidirectional encoder representations from transformers (BERT): an empirical case study for Turkish

TAC at SemEval-2020 Task 12: Ensembling Approach for Multilingual Offensive Language Identification in Social Media

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