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DHMFRD - TER: a deep hybrid model for fake review detection incorporating review texts, emotions, and ratings

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第2期83卷 4533-4549页

作者： Duma, Ramadhani Ally Niu, Zhendong Nyamawe, Ally Tchaye-Kondi, Jude Chambua, James Yusuf, Abdulganiyu Abdu Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Univ Dodoma Coll Informat & Virtual Educ Dodoma Tanzania Univ Dar Es Salaam Comp Sci & Engn Dept Dar Es Salaam Tanzania

Recently, there has been an increasing reward to manipulate product/ service reviews, mostly profit-driven, since positive reviews infer high business returns and vice versa. To combat this issue, experts in industry and researchers recently attempted integrating multi-aspect (reviewer- and review-centric) data features. However, the emotions hidden in the review, the semantic meaning of the review, and data heterogeneity still deserve more study as they are essential indicators of fake content. This study proposed a Deep Hybrid Model for Fake Review Detection incorporating review Texts, Emotions, and Ratings (DHMFRD - TER). Initially, it computes contextualized review text vectors and extraction of emotion indicators representations. Then, the model learns the representation to extract higher-level review features. Finally, contextualized word vectors, ratings, and emotions are concatenated;such a multidimensional feature representation is used to classify reviews. Extensive experiments on three publicly available datasets demonstrate that DHMFRD-TER significantly outperforms state-of-the-art baseline approaches, achieving an accuracy of 0.988, 0.987, and 0.994 in Amazon, Yelp CHI, and OSF datasets, respectively.

关键词： Fake reviews Ratings Emotions Convolution neural network Long short-term memory bidirectional encoder representations from transformers

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Detection of Hate Speech and Offensive Language CodeMix Text in Dravidian Languages Using Cost-Sensitive Learning Approach

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IEEE ACCESS 2024年 12卷 20064-20090页

作者： Sreelakshmi, K. Premjith, B. Chakravarthi, Bharathi Raja Soman, K. P. Amrita Vishwa Vidyapeetham Amrita Sch Artificial Intelligence Coimbatore 641112 India Univ Galway Sch Comp Sci Galway H91 TK33 Ireland

Recently, the emergence of social media has opened the way for online harassment in the form of hate speech and offensive language. An automated approach is needed to detect hate and offensive content from social media, which is indispensable. This task is challenging in the case of social media posts or comments in low-resourced CodeMix languages. This paper investigates the efficacy of various multilingual transformer-based embedding models with machine learning classifiers for detecting hate speech and offensive language (HOS) content in social media posts in CodeMix Dravidian languages that belong to the low-resource language group. Experiments were conducted on six sets of openly available datasets in Kannada-English, Malayalam-English and Tamil-English languages. The objective is to identify a single pre-trained embedding model that commonly works well for HOS tasks in the above mentioned languages. For this, a comprehensive study of various multilingual transformer embedding models, such as BERT, DistilBERT, LaBSE, MuRIL, XLM, IndicBERT, and FNET for HOS detection was conducted. Our experiments revealed that MuRIL pre-trained embedding performed consistently well for all six datasets using Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. In a set of experiments conducted on six datasets, the highest accuracy results for each dataset are as follows: DravidianLangTech 2021 achieved 96% accuracy for Malayalam, 72% accuracy for Tamil, and 66% accuracy for Kannada. For HASOC 2021 Tamil, the accuracy reached 76%, and for HASOC 2021 Malayalam, it reached 68%. Additionally, HASOC 2020 demonstrated an accuracy of 92% for Malayalam. Moreover, we performed an in-depth error analysis and a comparative study, presenting a tabulated summary of our work compared to other top-performing studies. In addition, we employed a cost-sensitive learning approach to address the class imbalance problem in the dataset, in which minority classes get higher classific

关键词： Natural language processing CodeMix hate speech offensive language bidirectional encoder representations from transformers language-agnostic BERT sentence embedding multilingual representations for Indian languages machine learning IndicBERT

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TAQS: An Arabic Question Similarity System Using Transfer Learning of BERT With BiLSTM

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IEEE ACCESS 2022年 10卷 91509-91523页

作者： Alshammari, Waad AlHumoud, Sarah Imam Mohammad Ibn Saud Islamic Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11652 Saudi Arabia

With the rapid increase of Arabic content on the web comes an increased need for short and accurate answers to queries. Machine question answering has appeared as an important emerging field for progress in natural language processing techniques. Machine learning performance surpasses that of humans in some areas, such as natural language processing and text analysis, especially with large amounts of data. There are two main contributions of this research. First, we propose the Tawasul Arabic question similarity (TAQS) system with four Arabic semantic question similarity models using deep learning techniques. Second, we curated and used an Arabic customer service question-similarity dataset with a 44,404 entries of question-answer pairs, called "Tawasul." For TAQS, first, we use transfer learning to extract the contextualized bidirectional encoder representations from transformers (BERT) embedding with bidirectional long short-term memory (BiLSTM) in two different ways. Specifically, we propose two architectures: the BERT contextual representation with BiLSTM (BERT-BiLSTM) and the hybrid transfer BERT contextual representation with BiLSTM (HT-BERT-BiLSTM). The hybrid transfer representation combines two transfer learning techniques. Second, we fine-tuned two versions of bidirectional encoder representations from transformers for Arabic language (AraBERT). The results show that the HT-BERT-BiLSTM with the features of Layer 12 reaches an accuracy of 94.45%, where the fine-tuning of AraBERTv2 and AraBERTv0.2 achieve 93.10% and 93.90% accuracy, respectively, for the Tawasul dataset. Our proposed TAQS model surpassed the performance of the state-of-the-art BiLSTM with SkipGram by a gain of 43.19% in accuracy.

关键词： Neural networks Semantics Bit error rate Question answering (information retrieval) transformers Natural language processing Deep learning Ranking (statistics) Deep learning Text analysis Encoding Natural language processing question answering question similarity question ranking deep learning neural network bidirectional encoder representations from transformers word embedding

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A Hybrid BERT-CNN Approach for Depression Detection on Social Media Using Multimodal Data

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COMPUTER JOURNAL 2024年第7期67卷 2453-2472页

作者： Beniwal, Rohit Saraswat, Pavi Delhi Technol Univ Dept Comp Sci & Engn Main Bawana Rd Delhi 10042 India

Due to the absence of early facilities, a large population is dealing with stress, anxiety, and depression issues, which may have disastrous consequences, including suicide. Past studies revealed a direct relationship between the high engagement with social media and the increasing depression rate. This research initially creates a dataset with text, emoticons and image data, and then preprocessing is performed using diverse techniques. The proposed model in the research consists of three parts: first is textual bidirectional encoder representations from transformers (BERT), which is trained on only text data and also emoticons are converted into a textual form for easy processing;second is convolutional neural network (CNN), which is trained only on image data;and the third is the combination of best-performing models, i.e. hybrid of BERT and CNN (BERT-CNN), to work on both the text and images with enhanced accuracy. The results show the best accuracy with BERT, i.e. 97% for text data;for image data, CNN has attained the highest accuracy of 89%. Finally, the hybrid approach is compared with other combinations and previous studies;it achieved the best accuracy of 99% in the categorization of users into depressive and non-depressive based on multimodal data.

关键词： depression detection social media machine learning deep learning bidirectional encoder representations from transformers convolutional neural network

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Enhanced character embedding for Chinese named entity recognition

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MEASUREMENT & CONTROL 2020年第9-10期53卷 1669-1681页

作者： Jia, Bingjing Wu, Zhongli Wu, Bin Liu, Yutong Zhou, Pengpeng Beijing Univ Posts & Telecommun Sch Comp Sci 10 Xitucheng Rd Beijing 100876 Peoples R China Anhui Sci & Technol Univ Sch Comp Sci Bengbu Peoples R China

Traditional named entity recognition methods mainly explore the application of hand-crafted features. Currently, with the popularity of deep learning, neural networks have been introduced to capture deep features for named entity recognition. However, most existing methods only aim at modern corpus. Named entity recognition in ancient literature is challenging because names in it have evolved over time. In this paper, we attempt to recognise entities by exploring the characteristics of characters and strokes. The enhanced character embedding model, named ECEM, is proposed on the basis of bidirectional encoder representations from transformers and strokes. First, ECEM can generate the semantic vectors dynamically according to the context of the words. Second, the proposed algorithm introduces morphological-level information of Chinese words. Finally, the enhanced character embedding is fed into the bidirectional long short term memory-conditional random field model for training. To explore the effect of our proposed algorithm, experiments are carried out on both ancient literature and modern corpus. The results indicate that our algorithm is very effective and powerful, compared with traditional ones.

关键词： Enhanced character embedding stroke bidirectional encoder representations from transformers named entity recognition

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A Long-Text Classification Method of Chinese News Based on BERT and CNN

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IEEE ACCESS 2022年 10卷 34046-34057页

作者： Chen, Xinying Cong, Peimin Lv, Shuo Dalian Jiaotong Univ Sch Comp & Commun Engn Dalian 116028 Peoples R China

Text Classification is an important research area in natural language processing (NLP) that has received a considerable amount of scholarly attention in recent years. However, real Chinese online news is characterized by long text, a large amount of information and complex structure, which also reduces the accuracy of Chinese long text classification as a result. To improve the accuracy of long text classification of Chinese news, we propose a BERT-based local feature convolutional network (LFCN) model including four novel modules. First, to address the limitation of bidirectional encoder representations from transformers (BERT) on the length of the max input sequence, we propose a named Dynamic LEAD-n (DLn) method to extract short texts within the long text based on the traditional LEAD digest algorithm. In Text-Text encoder (TTE) module, we use BERT pretrained language model to complete the sentence-level feature vector representation of a news text and to capture global features by using the attention mechanism to identify correlated words in text. After that, we propose a CNN-based local feature convolution (LFC) module to capture local features in text, such as key phrases. Finally, the feature vectors generated by the different operations over several different periods are fused and used to predict the category of a news text. Experimental results show that the new method further improves the accuracy of long text classification of Chinese news.

关键词： Bit error rate Task analysis Convolutional neural networks Feature extraction Lead transformers Convolution bidirectional encoder representations from transformers Chinese long text classification convolutional neural network natural language processing representation learning

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RETRACTED: Diverse ensemble classifier driven Email spam classification using multiple word embedding's with COCOB optimizer (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 2941-2954页

作者： Vinitha, V. Sri Renuka, D. Karthika Bannari Amman Inst Technol Dept IT Sathyamangalam Tamil Nadu India PSG Coll Technol Dept IT Coimbatore Tamil Nadu India

Spam Email is a serious concern which can steal user's personal information and cause huge financial loss due to the increasing rate of internet users. Therefore, the demand for accurate spam filtering has become more sophisticated for the Email spam detection. In the existing techniques, it is difficult to intricate the relationship between words in the Email using certain word embedding techniques and learning rate tuning is one of the greatest challenges of stochastic optimization. To overcome this difficulty, the proposed framework uses diverse ensemble based Email spam classification by incorporating multiple word embedding's with Continuous Coin Betting optimizer. Word2Vec is used to produce the first set of 200D, next set of 200Dword embedding is produced by Glove and 768D is produced by using bidirectional encoder representations from transformers (BERT) respectively. After generating word embedding, then it is classified through diverse ensemble based classifier with base level classifier consists of Long Short Term Memory (LSTM) Networks, Gated Recurrent Unit (GRU) and Bi-directional Gated Recurrent Unit (Bi-GRU) and LSTM as Meta-classifier using COCOB optimizer. Experiments were conducted on 3 benchmark Email dataset and result shows that the proposed system outperforms well with a low false positive rate.

关键词： Word2Vec bidirectional encoder representations from transformers global vectors gated recurrent unit bidirectional gated recurrent unit long short term memory continuous coin betting

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G-BERT: An Efficient Method for Identifying Hate Speech in Bengali Texts on Social Media

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IEEE ACCESS 2023年 11卷 79697-79709页

作者： Keya, Ashfia Jannat Kabir, Md. Mohsin Shammey, Nusrat Jahan Mridha, M. F. Islam, Md. Rashedul Watanobe, Yutaka Bangladesh Univ Business & Technol Dept Comp Sci & Engn Dhaka 1216 Bangladesh Univ Girona Super Polytech Sch Girona 17071 Spain Amer Int Univ Bangladesh Dept Comp Sci & Engn Dhaka 1229 Bangladesh Univ Asia Pacific Dept Comp Sci & Engn Dhaka 1205 Bangladesh Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu 9658580 Japan

The rapid increase in Internet users has increased online concerns such as hate speech, abusive texts, and harassment. In Bangladesh, hate text in Bengali is frequently used on various social media platforms to condemn and abuse individuals. However, Research on recognizing hate speech in Bengali texts is lacking. The pervasive negative impact of hate speech on individuals' well-being and the urgent need for effective measures to address hate speech in Bengali texts have created a significant research gap in the Bengali hate speech detection field. This study suggests a technique for identifying hate speech in Bengali social media posts that may harm individuals' sentiments. Our approach utilizes the bidirectional encoder representations from transformers (BERT) architecture to extract Bengali text properties, whereas hate speech is categorized using a Gated Recurrent Units (GRU) model with a Softmax activation function. We propose a new model, G-BERT, that combines both models. We compared our model's performance with several other algorithms and achieved an accuracy, precision, recall, and F1-score of 95.56%, 95.07%, 93.63%, and 92.15%, respectively. Our proposed model outperformed all other classification algorithms tested. Our findings show that the strategy we have suggested is successful in locating hate speech in Bengali texts posted on social media platforms, which can aid in mitigating online hate speech and promoting a more respectful online environment.

关键词： bidirectional encoder representations from transformers deep learning hate speech gated recurrent unit social platform

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BSTFNet:An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

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Computers, Materials & Continua 2024年第3期78卷 3929-3951页

作者： Hong Huang Xingxing Zhang Ye Lu Ze Li Shaohua Zhou School of Computer Science and Engineering Sichuan University of Science&EngineeringYibin644002China School of Mathematics and Statistics Sichuan University of Science&EngineeringYibin644002China

While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious *** address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the bidirectional encoder representations from transformers(BERT)*** the byte-level granularity,we initially employ the bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial *** fusion of features from both granularities serves as the ultimate multidimensional representation of malicious *** approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut *** experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.

关键词： Encrypted malicious traffic classification bidirectional encoder representations from transformers text convolutional neural network bidirectional gated recurrent unit

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SignBERT: A BERT-Based Deep Learning Framework for Continuous Sign Language Recognition

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IEEE ACCESS 2021年 9卷 161669-161682页

作者： Zhou, Zhenxing Tam, Vincent W. L. Lam, Edmund Y. Univ Hong Kong Dept Elect & Elect Engn Pokfulam Hong Kong Peoples R China

Continuous sign language recognition (CSLR) is a very challenging task in intelligent systems, since it requires to produce real-time responses while performing computationally intensive video analytics and language modeling. Previous studies mainly focus on adopting hidden Markov models or recurrent neural networks with a limited capability to model specific sign languages, and the accuracy can drop significantly when recognizing the signs performed by different signers with non-standard gestures or non-uniform speeds. In this work, we develop a deep learning framework named SignBERT, integrating the bidirectional encoder representations from transformers (BERT) with the residual neural network (ResNet), to model the underlying sign languages and extract spatial features for CSLR. We further propose a multimodal version of SignBERT, which combines the input of hand images with an intelligent feature alignment, to minimize the distance between the probability distributions of the recognition results generated by the BERT model and the hand images. Experimental results indicate that when compared to the performance of alternative approaches for CSLR, our method has better accuracy with significantly lower word error rate on three challenging continuous sign language datasets.

关键词： Feature extraction Assistive technologies Hidden Markov models Gesture recognition Bit error rate Training Three-dimensional displays bidirectional encoder representations from transformers continuous sign language recognition deep learning video analytics

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