Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer

Turki, Turki; Roy, Sanjiban Sekhar

doi:10.3390/app12136611

Cited by 27 publications

(14 citation statements)

References 47 publications

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“…The bigger the term in the cloud, the more often it appears in the original text. Words with more significant font sizes are considered more important or crucial to the overall message [25]. Publications have fluctuated during observation.…”

Section: Methodsmentioning

confidence: 99%

Social Media: The New Frontier for Human Resource Management in Asia

Rajiani,

Arisanty,

Riana

2024

Human Behavior and Emerging Technologies

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Social media has become an increasingly vital tool for human resource management (HRM) in many parts of the globe. However, Asian societies have adopted social media for HRM at a lesser rate than Western cultures, which are more egalitarian and open, leading to greater comfort with using social media for professional interactions, even with superiors. This article provides a comprehensive literature review on the use of social media in HRM in Asian societies. The review analyzes 590 studies published between 2013 and 2023, following the PRISMA protocol for systematic reviews and using VOSviewer. The results indicate that the number of publications on this topic has fluctuated, with a notable increase in interest since 2015. The most prolific countries in terms of publications are India, China, Malaysia, Indonesia, Saudi Arabia, Pakistan, Taiwan, South Korea, Thailand, and the UAE. The study identifies significant research clusters and discusses the difficulties encountered when implementing social media technologies in HRM within an Asian context. These obstacles include cultural factors such as collectivism, power distance, and privacy concerns. The controversial findings regarding the distinction between excellent research and practical implementation demonstrate the need for additional research to understand better the potential benefits and challenges of incorporating social media into HRM practices in the region.

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Section: Methodsmentioning

confidence: 99%

Social Media: The New Frontier for Human Resource Management in Asia

Rajiani,

Arisanty,

Riana

2024

Human Behavior and Emerging Technologies

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“…In the embedding stage, one of the most state-of-the-art embedding models, Sentence Embeddings using Siamese BERT (SBERT), which is a modified BERT network that incorporates Siamese and triplet networks to produce semantically meaningful sentence embeddings, was used [40]; specifically, the allmpnet-base-v2 sentence-transformer model was employed based on its outstanding performance scores. Using the embedding results, we built three supervised models, which utilized three different machine learning algorithms-logistic regression, support vector machine (SVM), and random forest-all of which are commonly used in the relevant research literature [e.g., [41][42][43].…”

Section: Plos Onementioning

confidence: 99%

Where do cross-cutting discussions happen?: Identifying cross-cutting comments on YouTube videos of political vloggers and mainstream news outlets

Chae,

Lee

2024

PLoS ONE

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Since the conception of social media, research on political communication has pointed toward the risk that the social media environment can foster political echo chambers. However, this has recently been contradicted by some studies demonstrating “cross-cutting discussions” on social media. The current study extends this literature by particularly focusing on communication on political vlogger videos and having mainstream news outlet videos as a reference point. Specifically, this study addresses five points: (1) to what extent cross-partisan comments occupy conservative and liberal vloggers’ comment threads and if there is a significant difference between the two, (2) the possibility that comments from vlogger videos can be utilized to predict the political leanings of comments on mainstream news outlet videos, (3) if the proportion of cross-cutting discussions on mainstream news outlet videos significantly varies by the news outlet’s political leaning, (4) if a neutral news outlet channel can work as a venue for cross-cutting discussions, and (5) if the proportion of cross-cutting comments in mainstream news outlet comment threads is significantly different from that in vlogger comment threads. Both manual and computational analyses were employed; the political leanings of vlogger comments were analyzed by manual content analysis, and based on the results, the political leanings of mainstream news outlet comments were analyzed by NLP classifiers using three different algorithms—logistic regression, SVM, and random forest. As a result, we found that the proportion of cross-cutting discussions significantly varies by both the channel’s political leaning and media type. In addition, our results suggest the possibility of neutral news outlets as a place for cross-cutting discussions.

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“…Teknik kedua yang digunakan adalah Count vectorizer yang merupakan teknik feature extraction dan berperan dalam menggambarkan koleksi kata dalam bentuk matriks-matriks [18]. Penelitian berkaitan dengan klasifikasi teks condong menggunakan teknik ini, sebagaimana telah digunakan dalam penelitian sebelumnya mengenai klasifikasi sentimen pesan berindikasi cyberbullying [19].…”

Section: Teknik Yang Digunakanunclassified

Optimalisasi Model Klasifikasi Sentimen Netizen Terhadap Merek Tas Luar Negeri

et al. 2023

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Abstract Research on text mining has grown more than ever in various sectors. Public figures have also grown in interest towards the field and have the tendency to get to know more about consumers’ perceptions toward relevant goods and the reputation of an individual in social media. Sentiment analysis is a state-of-the-art technique that can be utilized to evaluate such trends or general views, for instance the reputation of a fashion brand. The dataset is built upon the crawled tweets that are relevant with the required topics which have the purpose to analyze the preferred fashion brand of the public. This study shows that the public leads to a positive notion toward foreign bag brands. The algorithms that are being compared includes Logistic Regression, Multinomial Naïve Bayes, Decision Tree, K-Nearest Neighbors, Random Forest, and Support Vector Machine. Support Vector Machine provides the best model which reaches 69% in accuracy. The Synthetic Minority Oversampling Technique (SMOTE) was also conducted to improve the model. Result shows that the Support Vector Machine model has successfully increased its accuracy by 13%, reaching an accuracy of 82%. Keywords: Sentiment Analysis, Brand, Machine Learning, Classification, SMOTE Abstrak Penelitian mengenai text mining telah mengalami peningkatan dibanding sebelumnya di dalam berbagai sektor. Figur publik juga semakin tertarik terhadap bidang tersebut dan memiliki kecenderungan untuk mengetahui lebih banyak mengenai persepsi konsumen terhadap suatu barang dan mengenai reputasi seseorang di media sosial. Sentimen analisis merupakan sebuah teknik state-of-the-art yang dapat digunakan untuk mengevaluasi suatu tren atau pandangan umum mengenai suatu hal, misalnya reputasi sebuah merek fashion. Sumber himpunan data yang digunakan pada penelitian ini dibuat berdasarkan crawling tweet yang relevan dengan topik yang dibutuhkan, yang bertujuan untuk menganalisis merek fashion yang disukai oleh masyarakat. Penelitian ini menunjukkan bahwa persepsi masyarakat mengarah pada persepsi positif terhadap merek tas luar negeri. Pada penelitian ini, beberapa algoritma digunakan sebagai perbandingan, antara lain Logistic Regression, Multinomial Naïve Bayes, Decision Tree, K-Nearest Neighbors, Random Forest, dan Support Vector Machine. Hasil pengujian model menunjukkan algoritma Support Vector Machine memiliki performa terbaik dengan accuracy sebesar 69%. Kemudian digunakan teknik Synthetic Minority Oversampling Technique (SMOTE) untuk meningkatkan performa dari model. Hasil menunjukkan bahwa model algoritma Support Vector Machine telah berhasil ditingkatkan dengan accuracy sebesar 13%, mencapai accuracy sebesar 82%. Kata kunci: Sentimen Analisis, Merek, Pembelajaran Mesin, Klasifikasi, SMOTE

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Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer

Cited by 27 publications

References 47 publications

Social Media: The New Frontier for Human Resource Management in Asia

Social Media: The New Frontier for Human Resource Management in Asia

Where do cross-cutting discussions happen?: Identifying cross-cutting comments on YouTube videos of political vloggers and mainstream news outlets

Optimalisasi Model Klasifikasi Sentimen Netizen Terhadap Merek Tas Luar Negeri

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