Feature Expansion Using Word2vec for Hate Speech Detection on Indonesian Twitter with Classification Using SVM and Random Forest

Dewi, Mila Putri Kartika; Setiawan, Erwin Budi

doi:10.30865/mib.v6i2.3855

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…We carefully reviewed each document to obtain the key information of each work. In this part, we focus on [11], [30], [17], [23], [12], [28], [27], [21], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40]…”

Section: B What Has Been Done So Far In Indonesian Abusive Language D...mentioning

confidence: 99%

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Pamungkas¹,

Putri²,

Fatmawati³

2023

IJACSA

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This study aims to provide an overview of the current research on detecting abusive language in Indonesian social media. The study examines existing datasets, methods, and challenges and opportunities in this field. The research found that most existing datasets for detecting abusive language were collected from social media platforms such as Twitter, Facebook, and Instagram, with Twitter being the most commonly used source. The study also found that hate speech is the most researched type of abusive language. Various models, including traditional machine learning and deep learning approaches, have been implemented for this task, with deep learning models showing more competitive results. However, the use of transformer-based models is less popular in Indonesian hate speech studies. The study also emphasizes the importance of exploring more diverse phenomena, such as islamophobia and political hate speech. Additionally, the study suggests crowdsourcing as a potential solution for the annotation approach for labeling datasets. Furthermore, it encourages researchers to consider code-mixing issues in abusive language datasets in Indonesia, as it could improve the overall model performance for detecting abusive language in Indonesian data. The study also suggests that the lack of effective regulations and the anonymity afforded to users on most social networking sites, as well as the increasing number of Twitter users in Indonesia, have contributed to the rising prevalence of hate speech in Indonesian social media. The study also notes the importance of considering code-mixed language, out-of-vocabulary words, grammatical errors, and limited context when working with social media data.

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Section: B What Has Been Done So Far In Indonesian Abusive Language D...mentioning

confidence: 99%

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Pamungkas¹,

Putri²,

Fatmawati³

2023

IJACSA

View full text Add to dashboard Cite

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“…Shofianina Dwi Ananda Putri et al used Naive Bayes, SVM and other machine learning approach to detect hate speech and offensive language from Sundanese and Javanese Indonesian local language [9]. Mila Putri Kartika Dewi et al implemented a feature expansion system from comments using Word2Vec, Bag of Word (BOW) and TF-IDF [10]. Nurtheri Cahyana et al proposed an automatic annotation system using K-Nearest Neighbor to annotate comments datasets easily [11].…”

Section: Literature Studymentioning

confidence: 99%

Performance Analysis of Logistic Regression, Naive Bayes, KNN, Decision Tree, Random Forest and SVM on Hate Speech Detection from Twitter

Das,

Bhattacharyya,

Sarkar

2023

IRJIET

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Hate speech specially racism, gender and religion discrimination, defaming comments are becoming one of the biggest problems in Twitter these days, that are making people to switch to other social media. Its effect is long-standing and unpreventable. To stop hateful activities from happening, Machine Learning approaches are needed to be applied. This research article focuses on the performance analysis and effectiveness of Logistic Regression, Gaussian Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest and Support Vector Machine on detection of hate speech from Twitter. SVM, Decision Tree and Random Forest outperformed all the other models, achieving state-of-art 95.5%, 96.2% and 98.2% accuracy respectively on comments gather over a stretch.

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