Classification of Toxicity in Comments using NLP and LSTM

Garlapati, Anusha; Malisetty, Neeraj; Narayanan, Gayathri

doi:10.1109/icaccs54159.2022.9785067

Cited by 12 publications

(4 citation statements)

References 7 publications

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“…These models provide heightened efficiency while dealing with NLP problems such as sentimental analysis, as described in the work of Jeoldar et al [86] for classification and topic discovery. Garlapati et al [87] provide a novel approach to applying NLP to detect toxic comments on social media platforms. In a similar fashion Onan et al [88] a language model for detecting sarcasm in speech with help of a Bidirectional LSTM network.…”

Section: Lstm and Blstm Modelsmentioning

confidence: 99%

“…In a similar fashion Onan et al [88] a language model for detecting sarcasm in speech with help of a Bidirectional LSTM network. Klein et al [59] Clinical epidemiology Ni et al [60] Risk analysis Tong et al [61] Medical Informatics Qin et al [62] Title and abstract screening Balyan et al [63] Text classification Multi-layer Perceptron models Heo et al [65] Stroke prediction Zeng et al [66] Medical Informatics Convolutional Neural Networks Jin et al [70] Sentiment classification Tahir et al [71] Gene promoter identification Sharma et al [72] Image captioning Recurrent Neural Networks Zhou et al [74] Pre-diagnostic service Basiri et al [75] Sentiment analysis Auto-encoders Drozdov et al [77] Syntactic parsing Yu et al [78] Answer retrieval Duong et al [79] Recommendation system Yan et al [80] Question-answering Generative Adversarial Models Yang et al [82] Text generation Wang et al [83] Disease prediction Croce et al [84] Text classification LSTM and BLSTM Models Jeoldar et al [86] Sentiment Analysis Garlapati et al [87] Text-toxicity detection Onan et al [88] Sarcasm detection BERT-based Models Xu et al [90] Group classification Gao et al [91] Sentiment analysis Nugroho et al [92] News topic classification…”

Section: Lstm and Blstm Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Tyagi

Bhushan

2023

Wireless Pers Commun

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Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.

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Section: Lstm and Blstm Modelsmentioning

confidence: 99%

Section: Lstm and Blstm Modelsmentioning

confidence: 99%

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Tyagi

Bhushan

2023

Wireless Pers Commun

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show abstract

“…Removing stop words can increase the signal-to-noise ratio in unstructured text and thus increase the statistical significance of terms that may be important for a specific task [20]. Punctuation Removal, this flag -used to divide the text into sentences, paragraphs, and phrases -affects the result of any text processing approach, especially what depends on the frequency of occurrence of words and phrases because punctuation marks are often used in the text [21]. Most text and document data sets contain many unnecessary characters, such as punctuation and special characters [22].…”

Section: Preprocessingmentioning

confidence: 99%

Hate Speech Detection for Banjarese Languages on Instagram Using Machine Learning Methods

Alkaff,

Miqdad,

Fachrurrazi

et al. 2023

matrik

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Hate speech refers to verbal expression or communication that aims to provoke or discriminate against individuals. The Ministry of Communication and Information of Indonesia has encountered and dealt with 3,640 cases of hate speech transmitted through digital channels between 2018 and 2021. Particularly in South Kalimantan, hate speech in the local language, Banjarese has become increasingly prevalent in recent years. Surprisingly, there is a lack of research on using machine learning to detect hate speech in the Banjarese language, specifically on Instagram. Therefore, this study aimed to address this gap by constructing a dataset of Banjarese language hate speech and comparing various feature extraction and machine learning models to detect Banjarese language hate speech effectively. Thisresearch used several feature extraction techniques and machine learning methods to detect Banjareselanguage hate speech. The feature extraction methods used were Word N-Gram, Term Frequency- Inverse Document Frequency (TF-IDF), a combination of Word N-Gram and TF-IDF, Word2Vec, and Glove, while the machine learning methods used were Support Vector Machine (SVM), Na¨ıve Bayes, and Decision Tree. The results of this study revealed that the combination of TF-IDF for feature extraction and SVM as the model achieves exceptional performance. The average Recall, Precision, Accuracy, and F1-Score score exceeded 90%, demonstrating the model’s ability to identify Banjarese hate speech accurately.

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“…The proposed approach in the research conducted by Garlapati et al [13] also involves using a combination of feature engineering and deep learning models to classify toxic comments. To train an LSTM and CNN, the authors extract word embeddings, sentiment scores, and part-of-speech tags from the comments.…”

Section: B Deep Learning Architecturesmentioning

confidence: 99%

Toxic Comment Detection Using Bidirectional Sequence Classifiers

Maity,

More,

Patil

et al. 2023

Preprint

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<p>With the rising surge of online toxicity, automating the identification of abusive language becomes crucial for improving online discourse. This study proposes a deep learning system that efficiently uses multiple labels to classify harmful comments using bi-directional Long Short-Term Memory (LSTM) networks. By leveraging contextual information, the bi-LSTM model achieves state-of-the-art performance in classifying subtle forms of toxicity such as threats, insults, identity hate, and obscenity. The model achieves above 95\% accuracy on benchmark datasets with rigorous data processing, optimized neural architecture, and the utilization of FastText embeddings to handle words that are not in the vocabulary. This technique can automatically filter different levels of toxicity, promoting positive online interactions when integrated into online platforms. The proposed study outlines an end-to-end pipeline incorporating recent NLP advancements and deep contextualized language models to address contemporary challenges in AI-enabled content moderation.</p>

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Classification of Toxicity in Comments using NLP and LSTM

Cited by 12 publications

References 7 publications

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Hate Speech Detection for Banjarese Languages on Instagram Using Machine Learning Methods

Toxic Comment Detection Using Bidirectional Sequence Classifiers

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