Machine Learning based Automatic Hate Speech Recognition System

William, P.; Gade, Ritik; Chaudhari, Rup esh; Pawar, A. B.; Jawale, M. A.

doi:10.1109/icscds53736.2022.9760959

Cited by 52 publications

(8 citation statements)

References 20 publications

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“…Text classification also includes processing, which puts documents into predetermined categories [2]. Text classification can be done for solving several cases, such as sentiment analysis [3], emotion analysis [4], and hate speech detection [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Text Annotation for Hate Speech Detection using K-Nearest Neighbors and Term Frequency-Inverse Document Frequency

Cahyana¹,

Saifullah²,

Fauziah³

et al. 2022

IJACSA

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Sentiment analysis can detect hate speech using the Natural Language Processing (NLP) concept. This process requires annotation of the text in the labeling. However, when carried out by people, this process must use experts in the field of hate speech, so there is no subjectivity. In addition, if processed by humans, it will take a long time and allow errors in the annotation process for extensive data. To solve this problem, we propose an automatic annotation process with the concept of semi-supervised learning using the K-Nearest Neighbor algorithm. This process requires feature extraction of term frequency-inverse document frequency (TF-IDF) to obtain optimal results. KNN and TF-IDF were able to annotate and increase the accuracy of < 2% from the initial iteration of 57.25% to 59.68% in detecting hate speech. This process can annotate the initial dataset of 13169 with the distribution of 80:20 of training and testing data. There are 2370 labeled datasets; for testing, there are 1317 unannotated data; after preprocessing, there are 9482. The final results of the KNN and TF-IDF annotation processes have a length of 11235 for annotated data.

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Section: Introductionmentioning

confidence: 99%

Semi-supervised Text Annotation for Hate Speech Detection using K-Nearest Neighbors and Term Frequency-Inverse Document Frequency

Cahyana¹,

Saifullah²,

Fauziah³

et al. 2022

IJACSA

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“…In [14] proposes a machine learning-based automatic hate speech recognition system that can identify hate speech in real-time. The paper discusses the existing problem of hate speech on social media and the importance of addressing it.…”

Section: Literature Reviewmentioning

confidence: 99%

Hate Speech Detection in Social Media Using the Ensemble Learning Technique

Haque¹,

Chowdhury²

2023

Preprint

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“…To augment (i.e., increase the sample size of) tweets, we perform a character level augmentation (using KeyboardAug [24], OcrAug, and RandomAug [25] methods), word level augmentation (AntonymAug [25], Contextu-alWordEmbsAug, SpellingAug SplitAug, SynonymAug, TfIdfAug, WordEmbsAug and BackTranslationAug and ReservedAug), sentence level augmentation (using Contextual-WordEmbsForSentenceAug, AbstSummAug, and LambadaAug [26]). Figure 1 shows the steps in our framework; it includes 5 major steps [27][28][29][30][31][32][33][34]. The first step is to incorporate the Twitter data upon which a comprehensive pre-processing method has been carried out, afterwards extraction of features from the resulting pre-processed tweets has been accomplished.…”

Section: Dataset Preparation and Preprocessingmentioning

confidence: 99%

“…Figure 5 shows the most frequently used words in tweets. It can be seen that the word cloud has found the most common words for hate speech and non-hate speech that are associated with the tweets [30][31][32][33][34][35]. In Table 4, we show a sample of pre-processed and cleaned tweets.…”

Section: Distribution Frequent Words and Training Examples Of Tweetsmentioning

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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Machine Learning based Automatic Hate Speech Recognition System

Cited by 52 publications

References 20 publications

Semi-supervised Text Annotation for Hate Speech Detection using K-Nearest Neighbors and Term Frequency-Inverse Document Frequency

Semi-supervised Text Annotation for Hate Speech Detection using K-Nearest Neighbors and Term Frequency-Inverse Document Frequency

Hate Speech Detection in Social Media Using the Ensemble Learning Technique

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

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