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On Comparative Classification of Relevant Covid-19 Tweets 6

On Comparative Classification of Relevant Covid-19 Tweets

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6th international conference on Computer Science and Engineering, UBMK 2021

作者： Bakal, Gokhan Abar, Orhan Computer Engineering Department Abdullah Gul University Kayseri Turkey Computer Engineering Department Osmaniye Korkut Ata University Osmaniye Turkey

ISBN: (纸本)9781665429085

Due to the impressive information dissemination power of social networks such as Twitter, people tend to check social networks and web pages more than other traditional news sources, including newspapers, TV news programs, or radio channels. In that sense, the information carried by the content of the shared social media posts becomes much more considerable. However, most of the posts are commonly either irrelevant or inaccurate. Besides, the more critical case than the correctness of the information is the diffusion speed on Twitter through the reply or retweet actions. these activities make the initial situation even more complicated than itself due to the unregulated nature of the social networks and the lack of an immediate verification mechanism for the correctness of the posts. When we consider the current Covid-19 pandemic period (causing the coronavirus disease), one of the most utilized information resources is Twitter except the official health administration institutions. thereupon, examining the correctness of the information related to the Covid-19 pandemic by computational techniques (e.g., Data Mining, Machine Learning, and Deep Learning) has been gaining popularity and remains a substantial task. Hence, we mainly focused on analyzing the correctness of the posts related to the current pandemic shared on the Twitter platform. therefore, the overall goal of this work is to classify the relevant tweets using linear and non-linear machine learning models. We achieved the best F1 performance score (99%) with the neural network model using the unigram features & threshold value of 50 among all model configurations. © 2021 IEEE

关键词： Natural language processing systems

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Fake news detection algorithm based on incorporating multi-level features 23

Fake news detection algorithm based on incorporating multi-l...

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Proceedings of the 2023 6th international conference on Machine Learning and Natural language processing

作者： Jintao Su Junkuo Cao Ziyang Feng College of Information Science Technology Hainan Normal University China Information Network and Data Center China

ISBN: (纸本)9798400709241

With the development of social media, false news has become a serious problem facing today’s society. In view of the fact that most of the existing research on fake news detection technology relies on external knowledge sources, and there are still limitations in processing text details, this paper proposes a fake news detection algorithm (BCCU) that integrates multi-level features. First, the pre-trained model BERT is used to obtain global text features, and the BERT coding layer is used to access the capsule network to extract local text features. then a collaborative attention mechanism is introduced to process the redundant parts of the two text features and fuse the two. Finally User attribute data is added to assist in false news detection to improve the model’s ability to predict future news. Experimental results show that when this method only relies on news text, the accuracy on Weibo, Twitter15 and Twitter16 datasets are 1.1%, 13.3% and 11.4% higher than the classic model (RvNN) respectively.

关键词： BERT capsule network cooperative attention mechanism fake news user attributes

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Classification of advertisement articles using sentiment analysis: (Research-based on Korean natural language processing and deep learning technology) 22

Classification of advertisement articles using sentiment ana...

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Proceedings of the 2022 6th international conference on Natural language processing and information Retrieval

作者： Yongjun Kim Yung-Cheol Byun Computer Department Jeju National University South Korea

ISBN: (纸本)9781450397629

We live in a flood of big data and information through computers, communications, social media, and mass media. In other words, we can get the information we want quickly and easily, but we have many questions about the accuracy and reliability of this information. that is, there are many problems in trying to obtain accurate knowledge of such reckless details, and in particular, advertisement articles provided by online newspapers need to be clearer and more manageable when individuals try to find precise information and reports. Such experiences are threatened even to the foundation of existence due to distrust of Internet newspapers and advertisement evasion. To solve this problem, this study used emotion analysis of natural language processing to classify general and advertisement articles. Getting going Existing similar studies have mainly been undertaken to classify such advertisement articles, such as spam mail classification, and most of these studies used general natural language processing. However, this paper is a study that analyzes text data to understand further the meaning of the words, sentences, and phrases and adds steps to explore emotions to provide more accurate information that individuals want.

关键词： advertisement article database deep learning natural language processing sentiment analysis

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Fake News Detection and Classification Using Deep Learning Techniques for Social Media Text 6th

Fake News Detection and Classification Using Deep Learning T...

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6th international conference on advanced Informatics for Computing Research, ICAICR 2023

作者： Kumari Shalini, A. Saxena, Sameer Suresh Kumar, B. AMITY Institute of Information Technology Amity University Rajasthan Jaipur India School of Computer Science and Engineering Sanjay Ghodawat University Kolhapur India

ISBN: (纸本)9783031840616

the rise of social media networks has become a critical component of modern human living due to recent developments and advances in computer technology. the present context has evolved into a widely recognized medium for the distribution of information and news pertaining to a wide range of topics, including daily reports. this timeframe serves as the main period for data collecting and information transmission. this environment has a multitude of advantages;yet, it also entails a significant amount of misinformation and material that may potentially perplex both readers and users seeking accurate information. A significant concern associated with this system is to the limited availability of reliable information and real-world news pertaining to social media. An integrated system has been built that leverages several components of chain type of technology and natural language processing (NLP) to use machine learning techniques for the purpose of detecting false news and enhancing the accuracy of identifying fraudulent user accounts and postings. Various methodologies have been used to extract and choose features in order to provide effective training rules and appropriately assess the test classifier. A comprehensive empirical investigation demonstrates that the Twitter dataset is a useful resource for assessing the precision of fake news detection in classification tasks. the deep learning based RNN system demonstrates a high accuracy rate of 98.80% throughout both training and testing phases. this accuracy surpasses that attained by traditional machine learning methods such as SVM, ANN, and NB. Furthermore, the proposed methodology has the capability to detect superfluous information, a phenomenon that may be corroborated via several sources or by referencing current events. the prime purpose of this approach is to enhance the eminence of the material, identify any problematic sections within the text, and generate the original versions of those sections, all whi

关键词： Tweets

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Brain Cancer Ontology Construction 6th

Brain Cancer Ontology Construction

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6th international conference on Business Intelligence, CBI 2021

作者： El Hakym, Fatiha Mahi, Btissame Information Processing and Decision Support Laboratory Department of Computer Science Faculty of Science and Technology PO Box. 523 Beni Mellal Morocco

ISBN: (纸本)9783030765071

An ontology is an integral part of a semantic web. Ontology can be designed and create the necessary metadata elements to develop a semantic web applications. the evolution of semantic web has encouraged creation of ontologies in many domains. this work aims to create an ontology of brain cancer to do this we need to define the steps of creation of an ontology;first, we need a lot of information about brain cancer. this article describes the different steps involved in creating a brain cancer ontology. We have used a Protege 4. 2 to for the construction of a brain cancer ontology. © 2021, Springer Nature Switzerland AG.

关键词： Semantic web

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Design of Intelligent Guide System Based on Internet of things technology

Design of Intelligent Guide System Based on Internet of Thin...

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Internet of things, Automation and Artificial Intelligence (IoTAAI), international conference on

作者： Yinghan Hong Sirui Liang Guizhen Mai Jialuo Luo Ziying Huang Xingxing Li School of Physics and Electronic Engineering Hanshan Normal University Chaozhou China School of Computer and Information Engineering Hanshan Normal University Chaozhou China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

Despite societal and technological advancements, visually impaired individuals still face travel and daily life challenges. this paper proposes an intelligent guiding system combining software and hardware, integrating smart canes, glasses, and multifunctional backpacks. It addresses needs like traffic sign recognition, obstacle avoidance, and blind path usage. the software offers services through interaction, connection, and tracking modules. this system enhances independence and social integration, reduces reliance on caregivers, and promotes societal understanding and support for the visually impaired.

关键词： Glass Software Hardware User experience Natural language processing Safety Internet of things Collision avoidance Artificial intelligence Monitoring

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Classification of URDU headline news using Bidirectional Encoder Representation from Transformer and Traditional Machine learning Algorithm 6

Classification of URDU headline news using Bidirectional Enc...

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6th international Multi-Topic ICT conference, IMTIC 2021

作者： Mujahid, Kainat Bhatti, Sania Memon, Mohsin Mehran University of Engineering Technology Department of Software Engineering Jamshoro Pakistan

ISBN: (纸本)9781665482943

the web is full of unstructured data and getting the right information from that is a crucial problem. Recent advancements in the field of data mining on English Corpus provided the opportunity to automatically classify the text from different sources into broad categories. However, due to the lack of Urdu language resources, less attention has been paid to classifying text written in Urdu. Urdu is one of the most commonly spoken languages in Asian countries and also a national language of Pakistan. Urdu is a morphologically rich language, contains special diacritics, and free word order which makes it a difficult task for automatic text classification. In this work, we introduce the fine-tuning BERT architecture for the classification of Urdu headline news. Also, perform Logistics regression (LR) and multilayer perceptron (MLP) with Bert features. the proposed model classifies the Urdu headline news based on their predefined labels. We performed extensive experiments on Urdu news dataset iNLTK. Our proposed BERT-fine-tuned model outperforms baseline MLP and LG models and produces average accuracy for BERT, MLP, and LG with 95%, 94%, and 93% respectively. © 2021 IEEE.

关键词： Classification (of information)

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Sentiment Analysis for Product Reviews Using Deep Learning Techniques

Sentiment Analysis for Product Reviews Using Deep Learning T...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Neeraj Yadav Riya Kumbhar Angelo Goves Chrisel Coelho Irwin Rebello Siddesh Mahale Ameya Wadekar Shailendra Aswale Information Technology PCCE Goa University Verna India

Sentiment analysis or opinion mining, is the process of identifying and extracting subjective information from source materials. It is important in the context of user-generated content, such as tweets or Facebook posts, or Amazon product reviews, to understand the overall sentiment towards a certain topic or brand. this can provide valuable insights for businesses and organizations looking to improve their online reputation and engagement with customers. Traditionally, sentiment analysis has been handled through manual methods such as reading through and categorizing individual pieces of content. However, with the advent of big data and machine learning, automated methods have become more prevalent in the field. Methods such as natural language processing and machine learning algorithms are commonly used in sentiment analysis. this research paper focuses on a hybrid method for sentiment analysis on product reviews. Compared to other methods of deep learning and machine learning this hybrid approach excels in overall performance.

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Analysis of Traditional and Deep Learning Architectures in NLP: Towards Optimal Solutions

Analysis of Traditional and Deep Learning Architectures in N...

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international conference on Recent Trends in Advance Computing (ICRTAC)

作者： T Subbulakshmi Prathiba Lakshmi Narayan Shreejith Suthraye Gokulnath R. Suganya Girish H. Subramanian School of Computer Science and Engineering Vellore Institute of Technology Chennai India Information Systems School of Business Administration Pennsylvania State University Pennsylvania USA

In the ever-evolving landscape of Natural language processing (NLP), the development of novel architectures and optimization techniques has been instrumental in advancing the field. this survey paper presents a comprehensive exploration of traditional and deep learning architectures employed in NLP, while also delving into the optimization strategies that enhance their performance. With a primary focus on surveying existing literature, the aim is to provide a holistic view of the landscape of NLP architectures and their associated optimization techniques. Additionally, a novel architecture is introduced that promises to contribute to the ongoing progress in NLP. this architecture, detailed in our full paper, combines the best practices and innovations from traditional and deep learning models to tackle NLP tasks effectively. through this work, the aim is to contribute to the collective knowledge in NLP and facilitate future advancements in the field.

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A Personalized Federated Framework for Document-Level Biomedical Relation Extraction

A Personalized Federated Framework for Document-Level Biomed...

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Data-driven Optimization of Complex Systems (DOCS), international conference on

作者： Yan Xiao Yaochu Jin Haoyu Zhang Xu Huo Qiqi Liu Zeqi Zheng College of Information Engineering Shanghai Maritime University Shanghai China School of Engineering Westlake University Hangzhou China School of Information Science and Technology Hangzhou Normal University Hangzhou China School of Mathematics and Computer Science Zhejiang A&F University Hangzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Document-Ievel relation extraction (RE), a critical task in natural language processing, involves classifying relationships between entities mentioned in documents. Traditional document-level RE methods often rely on centralized datasets, which necessitates the collection of documents from various sources and their storage on a single machine. However, this brings up issues regarding privacy protection, and also limit the diversity of data. In this paper, we solve the document-level RE problem in federated learning scenario and propose a personalized federated learning framework named PF-BRE, specifically designed for biomedical RE. this framework enables multiple clients to collaboratively train a robust model without sharing the raw data. Additionally, to simulate the varying data characteristics of different clients in real-world application scenarios, we process the existing biomedical RE datasets to create distinct clients for personalized federated training. Exper-iments show that, compared to centralized training, the PF - BRE framework not only achieves impressive performance but also protects the data privacy of each client.

关键词： Training Data privacy Federated learning Biological system modeling Collaboration Natural language processing Data models Protection Complex systems Optimization

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