Offensive Language Detection on Social Media Based on Text Classification

Hajibabaee, Parisa; Malekzadeh, Masoud; Ahmadi, Mohsen; Heidari, Maryam; Esmaeilzadeh, Armin; Abdolazimi, Reyhaneh; Jones, James H.

doi:10.1109/ccwc54503.2022.9720804

Cited by 41 publications

(8 citation statements)

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“…This representation can be represented as one of three measures of word weights: TF [27], T-IDF [28] and a binary measure [29]. Based on such types of text representation, together with machine learning methods, the classification [30,31] was successfully studied.…”

Section: Text Representationmentioning

confidence: 99%

A New Method for Graph-Based Representation of Text in Natural Language Processing

2023

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Natural language processing is still an emerging field in machine learning. Access to more and more data sets in textual form, new applications for artificial intelligence and the need for simple communication with operating systems all simultaneously affect the importance of natural language processing in evolving artificial intelligence. Traditional methods of textual representation, such as Bag-of-Words, have some limitations that result from the lack of consideration of semantics and dependencies between words. Therefore, we propose a new approach based on graph representations, which takes into account both local context and global relationships between words, allowing for a more expressive textual representation. The aim of the paper is to examine the possibility of using graph representations in natural language processing and to demonstrate their use in text classification. An innovative element of the proposed approach is the use of common cliques in graphs representing documents to create a feature vector. Experiments confirm that the proposed approach can improve classification efficiency. The use of a new text representation method to predict book categories based on the analysis of its content resulted in accuracy, precision, recall and an F1-score of over 90%. Moving from traditional approaches to a graph-based approach could make a big difference in natural language processing and text analysis and could open up new opportunities in the field.

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Section: Text Representationmentioning

confidence: 99%

A New Method for Graph-Based Representation of Text in Natural Language Processing

2023

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“…Recently, researchers have investigated the detection of offensive language based on FCNN classification model as in Hajibabaee et al [3] as they obtained much better performance in terms of accuracy and F1 score. Machine-learning-based methods have also been proposed by Chia et al [4] for identifying cyberbullying.…”

Section: Related Workmentioning

confidence: 99%

“…This has generated huge interest among researchers, especially in automatic extraction, pre-processing, cognizing the sentiment and finally detecting the overall sentiment of the social media data. Natural language processing (NLP) combined with artificial intelligence and machine learning have been successful to some extent to address these challenges [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer

Turki

Roy

2022

Applied Sciences

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A plethora of negative behavioural activities have recently been found in social media. Incidents such as trolling and hate speech on social media, especially on Twitter, have grown considerably. Therefore, detection of hate speech on Twitter has become an area of interest among many researchers. In this paper, we present a computational framework to (1) examine out the computational challenges behind hate speech detection and (2) generate high performance results. First, we extract features from Twitter data by utilizing a count vectorizer technique. Then, we provide the labeled dataset of constructed features to adopted ensemble methods, including Bagging, AdaBoost, and Random Forest. After training, we classify new tweet examples into one of the two categories, hate speech or non-hate speech. Experimental results show (1) that Random Forest has surpassed other methods by generating 95% using accuracy performance results and (2) word cloud displays the most prominent tweets that are responsible for hateful sentiments.

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“…In our study. Author consider evaluting Brexit and NordStrom 2 (NS2 Gasprom) campaigns, and also author provided our dataset to the public [6][7][8].…”

Section: Related Workmentioning

confidence: 99%