Text Analysis For Hate Speech Detection Using Backpropagation Neural Network

Setyadi, Nabiila Adani; Nasrun, Muhammad; Setianingsih, Casi

doi:10.1109/iccerec.2018.8712109

Cited by 28 publications

(18 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Changing the variable Train set, test set, alpha, and epoch can show the highest results or accuracy done in previous research [6], [7]. Testing the Backpropagation model consists of, namely:  Train Set: The part of the dataset that we train to make predictions or run functions from another ML algorithm according to their respective goals  Test Set: The part of the dataset that we test to see its accuracy, or in other words, to see its performance.…”

Section: ) Backpropagation Model Testmentioning

confidence: 82%

Earthquake Early Warning System Prototype Based on Lot Using Backpropagation Algorithm

Pranesthi

Irawan

Setianingsih

2020

EECSI

Self Cite

View full text Add to dashboard Cite

Earthquakes are vibrations that occur on the earth's surface due to the sudden release of energy from the inside that creates seismic waves. An earthquake is caused by the movement of the earth's crust (the earth's plate). The frequency of a region refers to the type and size of earthquakes experienced during a period. Along with the development of early earthquake detection system technology provides a solution to minimize earthquake events. This research will discuss the system's design to determine the occurrence of earthquakes through time pattern analysis and Peak Ground Acceleration value. By using the Radial Basis Function Method, which later to minimize the loss of life from earthquakes. And help the main tools owned by the government. This study aims to determine the occurrence of earthquakes from Peak Ground Acceleration values and time analysis patterns, which are obtained from the decision of the Backpropagation method with an accuracy rate of 88%.

show abstract

Section: ) Backpropagation Model Testmentioning

confidence: 82%

Earthquake Early Warning System Prototype Based on Lot Using Backpropagation Algorithm

Pranesthi

Irawan

Setianingsih

2020

EECSI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Case folding bertujuan untuk men-general-kan keseluruhan kata pada teks untuk memudahkan proses selanjutnya. Selain itu penerapan proses ini dapat meningkatkan performa dari model dalam melakukan klasifikasi [12]. Hal ini dikarenakan dalam ASCII code, huruf besar dan kecil memiliki nilai yang berbeda sehingga perbedaan case pada karakter bisa membuat kata yang sama memiliki nilai token yang berbeda.…”

Section: Case Foldingunclassified

Identifikasi Ujaran Kebencian Multilabel Pada Teks Twitter Berbahasa Indonesia Menggunakan Convolution Neural Network

Dwitama

Hidayat²

2021

json

View full text Add to dashboard Cite

There has been a significant increase in communication activities between internet users in online media due to the increase in social media users. For instance, Twitter users may send messages via their tweets. However, tweets can also contain negative meanings. Therefore, it deserves special attention as it has the potential to contain hate speech. Even the government deems it necessary to publish regulations to deal with hate speech cases such as the Information and Electronic Transactions Law (ITE Law) issued in 2018 Article 28 paragraph 2 of the Hate Speech. Machine Learning (ML) is one of the techniques that can be used in identifying patterns. There are various types of data that ML can be applied to, including text (known as Text Analytic). Previous research has used the Support Vector Machine (SVM) method to identify hate speech on Twitter text with more than one label (multilabel). The purpose of this study was to identify hate speech on Twitter with a label of more than one (multilabel) via Convolutional Neural Network (CNN). The study obtained the best CNN model with an accuracy of 98.76% from the multi-label dataset on hate speech in Indonesian texts

show abstract

“…In a related study, Setyadi et al (2018) designed a back propagation neural network for hate speech detection in Twitter messages. The performance of the designed back propagation neural network was found to be average across all evaluation metrics.…”

Section: Related Work 21 Literature Reviewmentioning

confidence: 99%

Hate speech detection in Twitter using hybrid embeddings and improved cuckoo search-based neural networks

Ayo

Folorunso

Ibharalu

et al. 2020

IJICC

View full text Add to dashboard Cite

PurposeHate speech is an expression of intense hatred. Twitter has become a popular analytical tool for the prediction and monitoring of abusive behaviors. Hate speech detection with social media data has witnessed special research attention in recent studies, hence, the need to design a generic metadata architecture and efficient feature extraction technique to enhance hate speech detection.Design/methodology/approachThis study proposes a hybrid embeddings enhanced with a topic inference method and an improved cuckoo search neural network for hate speech detection in Twitter data. The proposed method uses a hybrid embeddings technique that includes Term Frequency-Inverse Document Frequency (TF-IDF) for word-level feature extraction and Long Short Term Memory (LSTM) which is a variant of recurrent neural networks architecture for sentence-level feature extraction. The extracted features from the hybrid embeddings then serve as input into the improved cuckoo search neural network for the prediction of a tweet as hate speech, offensive language or neither.FindingsThe proposed method showed better results when tested on the collected Twitter datasets compared to other related methods. In order to validate the performances of the proposed method, t-test and post hoc multiple comparisons were used to compare the significance and means of the proposed method with other related methods for hate speech detection. Furthermore, Paired Sample t-Test was also conducted to validate the performances of the proposed method with other related methods.Research limitations/implicationsFinally, the evaluation results showed that the proposed method outperforms other related methods with mean F1-score of 91.3.Originality/valueThe main novelty of this study is the use of an automatic topic spotting measure based on naïve Bayes model to improve features representation.

show abstract

Text Analysis For Hate Speech Detection Using Backpropagation Neural Network

Cited by 28 publications

References 0 publications

Earthquake Early Warning System Prototype Based on Lot Using Backpropagation Algorithm

Earthquake Early Warning System Prototype Based on Lot Using Backpropagation Algorithm

Identifikasi Ujaran Kebencian Multilabel Pada Teks Twitter Berbahasa Indonesia Menggunakan Convolution Neural Network

Hate speech detection in Twitter using hybrid embeddings and improved cuckoo search-based neural networks

Contact Info

Product

Resources

About