Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection

Isnain, Auliya Rahman; Sihabuddin, Agus; Suyanto, Yohanes

doi:10.22146/ijccs.51743

Cited by 36 publications

(28 citation statements)

References 5 publications

(8 reference statements)

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“…In this era, social media has become an important part of everyday life for the wider community [1]. Social media that are still widely used by people today, one of which is Twitter [2] [3]. Twitter is an online social networking and microblogging service that allows users to send and read text-based messages of up to 140 characters, known as Tweets [4].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

Isnain

Marga

Alita

2021

Indonesian J. Comput. Cybern. Syst.

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The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring the performance of the TF-IDF feature extraction and N-gram in machine learning uses the Naive Bayes method.The results of this study resulted in the accuracy rate of the Naive Bayes method with the TF-IDF feature selection. The total accuracy was 81% with a Precision value of 78%, Recall 91%, and f1-Score 84%. The highest results were obtained from the use of the Naive Bayes and Trigram algorithm parameters, namely 84%, namely 84% Precision, 86% Recall, and 85% f1-Score. The Naive Bayes algorithm with the use of the trigram type N-Gram feature extraction shows a fairly good performance in the process of classifying public tweet data.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

Isnain

Marga

Alita

2021

Indonesian J. Comput. Cybern. Syst.

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“…Each determination made does not have to use the same value, because fine-tuning is done so that the values used can be different in order to achieve the best results. Below shows figure of LSTM model from [21] . In LSTM, there are internal mechanisms called gates that can regulate the flow of information.…”

Section: Lstm Implementationmentioning

confidence: 99%

Long Short-Term Memory for Hate Speech and Abusive Language Detection on Indonesian Youtube Comment Section

2021

Proceedings of 2021 the 11th International Workshop on Computer Science and Engineering

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Hate speech is one of the most challenging problem internet is facing today. With increasing numbers of users online, hate speech also rise and takes time to be classified manually particularly in languages other than English. This research examines hate speech detection problem in form of Bahasa Indonesia. Millions of comments and text posts are added to various social media and discussion platforms. Manual classification in all of the internet as hate speech and offensive language is a near impossible and time-consuming task. This research uses Long Short-Term Memory (LSTM) and Bidirectional Long Short Term Memory (Bi-LSTM) for the method of classifying hate speech and abusive language. The final accuracy is 88,44% by using 200 neurons with Bi-LSTM method. Most common challenges are different languages, out of vocabulary words, long range dependencies, and sarcasm.

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“…Analisis sentimen adalah proses memahami dan mengolah data tekstual secara otomatis untuk mendapatkan informasi [5]. Analisis sentimen dilakukan untuk mendeteksi opini terhadap suatu subjek dan objek (misalnya individu, organisasi ataupun produk) dalam sebuah kumpulan data [6]. Besarnya pengaruh dan manfaat dari analisis sentimen menyebabkan penelitian ataupun aplikasi mengenai analisis sentimen berkembang pesat.…”

Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier

Alita

Isnain

2020

komputasi

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Kalimat sindiran atau sarkasme masih sering digunakan oleh kalangan publik untuk mengungkapkan maksud isi hati dan pikiran baik itu yang disampaikan secara langsng maupun tidak langsung. Sarkasme dilakukan untuk menyindir dan menyakiti hati seseorang dengan menggunakan bahasa atau kata yang didalamnya mengandung kata positif tetapi maknanya negatif sehingga sering sekali terjadi opini salah diklasifikasikan. Penelitian ini melakukan kombinasi antara proses sentimen analisis dengan deteksi sarkasme untuk pengklasifikasian opini yang terdapat pada Twitter. Proses analisis sentimen dilakukan dengan tahapan preprocessing dan ekstraksi fitur dan diklasifikan dengan menggunakan metode Support Vector Machine dilanjutkan dengan proses pendeteksian sarkasme yang dilakukan tahapan ekstraksi fitur dengan 4 set fitur yaitu sentiment related, punctuation-relate, lexical and syntactic, dan pattern-relate dan diklasifikasikan dengan menggunakan metode Random Forest Classifier. Hasil penelitian ini didapatkan peningkatan nilai rata-rata akurasi sebesar 16,61 %, nilai presisi sebesar 5,45 %, nilai recall sebesar 9,64% dan kenaikan nilai F1score sebesar 11,27% dengan jumlah data sebanyak 2.027 dengan rincian data dengan label positif berjumlah 1023, data dengan label negatif berjumlah 587 dan data dengan label netral berjumlah 462. Data sarkasme didapatkan dari tweet dengan label positif yang kemudian diberikan label sarkasme atau tidak sarkasme dan didapat hasil label dengan jumlah keseluruhan berlabel sarkasme berjumlah 354 dan tidak sarkasme berjumlah 669.

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Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection

Cited by 36 publications

References 5 publications

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

Long Short-Term Memory for Hate Speech and Abusive Language Detection on Indonesian Youtube Comment Section

Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier

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