BERT base model for toxic comment analysis on Indonesian social media

Nabiilah, Ghinaa Zain; Prasetyo, Simeon Yuda; Izdihar, Zahra Nabila; Girsang, Abba Suganda

doi:10.1016/j.procs.2022.12.188

Cited by 17 publications

(7 citation statements)

References 7 publications

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“…Seperti yang sudah dijabarkan di atas mengenai analisis perubahan makna dalam sosial media khususnya Instagram dan X. Platform media sosial di Indonesia mendorong terjadinya fenomena perubahan makna dengan hadirnya teknologi sehingga tercipta kebebasan dalam menyampaikan ide, kritik, saran, dan sebagainya yang tertuang pada komentar media sosial (Nabiilah et. al., 2023).…”

Section: Perubahan Makna Menjadi Lebih Burukunclassified

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Fenomena Perubahan Makna Kata Bahasa Indonesia Dalam Konten Platform Instagram Dan X

Ameylia Maya Kristinaupi,

Sitaresmi,

Sulistyaningsih

et al. 2024

Semant. J. Ilm. Progr. Stud. Pendidik. Bhs. dan Sastra Indones

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Media sosial adalah metode komunikasi yang berkembang pesat yang banyak digunakan oleh individu untuk interaksi tertulis dan lisan, serta untuk berbagi foto dan video. Hal ini menyebabkan perubahan makna, termasuk perluasan dan penyempitan, transformasi lengkap, penghalusan, dan pengasaran. Oleh karena itu, penulis melakukan penelitian dengan tujuan untuk menyelidiki fenomena pergeseran semantik yang terjadi di media sosial di era digital. Data untuk penelitian ini berasal dari Instagram dan X. Dataset yang digunakan dalam penelitian ini terdiri dari istilah-istilah bahasa Indonesia yang bersumber dari platform media sosial yang telah mengalami pergeseran semantik. Metodologi yang digunakan oleh para peneliti meliputi penggunaan prosedur penyimakan dan pencatatan. Metodologi yang digunakan adalah teknik kualitatif deskriptif. Melalui pemeriksaan data dan perdebatan yang terjadi, ditentukan bahwa ada lima kategori pergeseran semantik yang berbeda dalam penggunaan bahasa netizen Indonesia di Instagram dan X. Kelima kategori pergeseran semantik tersebut adalah perluasan, penyempitan, pengasaran, penghalusan, dan perubahan total. Hasil data menunjukkan bahwa terdapat 17 kata yang mengalami perubahan makna. Di antaranya, 3 kata mengalami kemunduran makna (disfemia), 7 kata mengalami perluasan makna, 2 kata mengalami perubahan penyempitan, 3 kata mengalami perubahan makna secara menyeluruh, dan 2 kata mengalami perubahan menjadi lebih halus.

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Section: Perubahan Makna Menjadi Lebih Burukunclassified

“…al., 2023). Terkadang komentar yang tertuang tersebut menimbulkan perdebatan kecil di antara netizen karena sering kali komentar yang muncul banyak mengandung makna baru yang rancu atau belum diketahui secara luas (Nabiilah et. al., 2023).…”

Section: Perubahan Makna Menjadi Lebih Burukunclassified

Fenomena Perubahan Makna Kata Bahasa Indonesia Dalam Konten Platform Instagram Dan X

Ameylia Maya Kristinaupi,

Sitaresmi,

Sulistyaningsih

et al. 2024

Semant. J. Ilm. Progr. Stud. Pendidik. Bhs. dan Sastra Indones

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“…Research category classification of scientific articles on human health risks of electromagnetic fields using pre-trained BERT [267]. BERT base model for toxic comment analysis on Indonesian social media [277]. Comparison of BERT implementations for natural language processing of narrative medical documents [296].…”

Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%

“…These models hold great promise for enhancing text-based interactions and insights. [245], [180], [188], [251], [261], [264], [271], [272], [274], [276], [277], [278], [279], [218], [288], [296], [297], [236], [299], [238] GPT Variants…”

Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Suryanto,

Wibawa,

Hariyono

et al. 2023

IJRCS

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Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.

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“…The optimal result of this study is using BERT on word embedding and classification with an F1 value of 84% in multi-class classification. Another study by Nabiilah et al [10] also used a pre-trained model with BERT architecture. This study compared several pre-trained models that had been trained with Indonesian language corpus data, namely multilingual BERT (MBERT), Indonesia BERT (IndoBERT), and Indonesia robustly optimized BERT pretraining approach (IndoRoBERTa) small.…”

Section: Introductionmentioning

confidence: 99%

Indonesian multilabel classification using IndoBERT embedding and MBERT classification

Nabiilah,

Alam,

Purwanto

et al. 2024

IJECE

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The rapid increase in social media activity has triggered various discussion spaces and information exchanges on social media. Social media users can easily tell stories or comment on many things without limits. However, this often triggers open debates that lead to fights on social media. This is because many social media users use toxic comments that contain elements of racism, radicalism, pornography, or slander to argue and corner individuals or groups. These comments can easily spread and trigger users vulnerable to mental disorders due to unhealthy and unfair debates on social media. Thus, a model is needed to classify comments, especially toxic ones, in Indonesian. Transformer-based model development and natural language processing approaches can be applied to create classification models. Some previous research related to the classification of toxic comments has been done, but the classification results of the model still require exploration to get optimal results. So, this research uses the proposed model by using different pre-trained models at the embedding and classification stages, in the embedding stage using Indonesia bidirectional encoder representations from transformers (IndoBERT), and classification using multilingual bidirectional encoder representations from transformers (MBERT). The proposed model provides optimal results with an F1 value of 0.9032.

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BERT base model for toxic comment analysis on Indonesian social media

Cited by 17 publications

References 7 publications

Fenomena Perubahan Makna Kata Bahasa Indonesia Dalam Konten Platform Instagram Dan X

Fenomena Perubahan Makna Kata Bahasa Indonesia Dalam Konten Platform Instagram Dan X

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Indonesian multilabel classification using IndoBERT embedding and MBERT classification

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