Hate Speech Detection Using Latent Semantic Analysis (LSA) Method Based On Image

Niam, Ilham Maulana Ahmad; Irawan, Budhi; Setianingsih, Casi; Putra, Bagas Prakoso

doi:10.1109/iccerec.2018.8712111

Cited by 13 publications

(10 citation statements)

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“…In Indonesia, the hate speech definition, rules, and laws are described in [37] . Examples that researchers used [37] as the hate speech definition basis for their research are [7] and [3] . In 2015, [30] further describes the definition of hate speech that has been given in [37] since they found that many citizens were against those hate speech rules.…”

Section: Current Research On Hate Speech and Abusive Language Detecti...mentioning

confidence: 99%

“…Despite the dataset used by [18] , [33] , and [50] not coming from a social media dataset (they build the speech dataset by manual recording), these studies can be used as a reference for research on social media speech dataset like the speech dataset from YouTube video. Last, for hate speech detection using an image dataset, currently, the research has been done by [3] .…”

Section: Current Research On Hate Speech and Abusive Language Detecti...mentioning

confidence: 99%

“…Besides using multiple annotators, there is a technique to reduce bias in annotation results. In [3] , their dataset was annotated by a linguistics expert to reduce the annotation bias. The linguistics expert came from Badan Pengembangan dan Pembinaan Bahasa (Badan Bahasa) Jawa Barat.…”

Section: Current Research On Hate Speech and Abusive Language Detecti...mentioning

confidence: 99%

See 2 more Smart Citations

Hate speech and abusive language detection in Indonesian social media: Progress and challenges

Ibrohim¹,

Budi²

2023

Heliyon

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Section: Current Research On Hate Speech and Abusive Language Detecti...mentioning

confidence: 99%

Section: Current Research On Hate Speech and Abusive Language Detecti...mentioning

confidence: 99%

See 1 more Smart Citation

Hate speech and abusive language detection in Indonesian social media: Progress and challenges

Ibrohim¹,

Budi²

2023

Heliyon

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“…Pada penelitian ini menggunakan 200 lagu teratas pada Spotify untuk periode Januari 2017 -Januari 2018 dengan 193 lagu berbeda yang menggunakan bahasa Indonesia dalam liriknya. Pada penelitian yang dilakukan oleh [6] menggunakan Latent Semantics Analysis (LSA) adalah untuk mengekstrak informasi dari sebuah gambar yang mengandung teks, kemudian mengklasifikasikan teks tersebut menjadi kata-kata yang memiliki ujaran kebencian atau tidak mengandung ujaran kebencian. Pada penelitian ini menggunakan data yang bersumber dari twitter.…”

Section: Pendahuluanunclassified

Pemodelan Topik Menggunakan n-Gram dan Non-negative Matrix Factorization

Afidh,

Syahrial

2023

jidt

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Pemodelan topik merupakan teknik pembelajaran mesin yang digunakan untuk melihat topik dalam sekumpulan dokumen teks. Pada penelitian ini pemodelan topik yang digunakan adalah Non-Negative Matrix Factorization (NMF) dengan n-gram. Preprocessing seperti penghilangan tanda baca, angka dan stopword diimplementasikan pada penelitian ini. Proses ini dilakukan dengan terlebih dahulu mengubah kata yang terdapat dalam artikel menjadi kata berhuruf kecil. Penelitian ini juga mengeksplorasi keefektifan penerapan unigram, bigram, dan trigram pada pemodelan topik. Pada penelitian ini juga menggunakan coherence value untuk menentukan jumlah topik terbaik yang dapat dibentuk. Data yang digunakan pada penelitian ini berjumlah 53.920 artikel berita yang bersumber dari portal berita RMOL.id dan BeritaSatu.com untuk periode Juli sampai Desember 2022. Visualisasi t-SNE digunakan untuk melihat distribusi pembentukan topik. Berdasarkan hasil penelitian yang dilakukan diperoleh bahwa jumlah topik yang dapat dibentuk dari RMOL.id untuk unigram adalah 15 topik dengan nilai coherence value 0.812748, bigram adalah 10 topik dengan nilai coherence value 0.835738 dan trigram adalah 7 topik dengan nilai coherence value 0.830572. Sedangkan pada BeritaSatu.com diperoleh 10 topik untuk unigram dengan nilai coherence value 0.799718, bigram 15 topik dengan nilai coherence value 0.788762 dan trigram 15 topik dengan nilai coherence value 0.801935.

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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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Hate Speech Detection Using Latent Semantic Analysis (LSA) Method Based On Image

Cited by 13 publications

References 0 publications

Hate speech and abusive language detection in Indonesian social media: Progress and challenges

Hate speech and abusive language detection in Indonesian social media: Progress and challenges

Pemodelan Topik Menggunakan n-Gram dan Non-negative Matrix Factorization

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

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