Purpose-The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter *** Twitter text is extracted by using named entity recognition(NER)with six classes hi...
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Purpose-The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter *** Twitter text is extracted by using named entity recognition(NER)with six classes hierarchy location in ***,the tweet then is classified into eight classes of natural disasters using the support vector machine(SVM).Overall,the system is able to classify tweet and mapping the position of the content ***/methodology/approach-This research builds a model to map the geolocation of tweet data using *** research uses six classes of NER which is based on region *** data is then classified into eight classes of natural disasters using the ***-Experiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data *** results also show good performance in geocoding such as match rate,match score and match ***,with SVM,this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region,which originate from the tweets *** limitations/implications-This study implements in Indonesia ***/value-(a)NER with six classes is used to create a location classification model with StanfordNER andArcGIS *** use of six location classes is based on the Indonesia regionalwhich has the large ***,it hasmany levels in its regional location,such as province,district/city,sub-district,village,road and place names.(b)SVMis used to classify natural *** of types of natural disasters is divided into eight:floods,earthquakes,landslides,tsunamis,hurricanes,forest fires,droughts and volcanic eruptions.
Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online *** study addresses challenges associated with small datasets and class imbalances in sarca...
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Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online *** study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and *** research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training *** inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original ***,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text *** exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original *** methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation ***,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using *** increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in *** proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.
This study proposes a gender classification method for Twitter data using a hybrid XLNet-fastText model. The objective is to enhance gender classification accuracy by leveraging the contextual understanding of XLNet a...
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The study focuses on the argument component in argumentation mining, specifically examining claim and premise types. Various datasets exist for argumentation components, each with different classes. The study evaluate...
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These days almost all people in the world use the Internet as the internet is constantly evolving. Cyber attack scale are increased thanks to cybercriminals that have become sophisticated in employing threats. Since t...
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Managing data has changed significantly because of cloud computing, which offers scalabe, flexible and reasonably priced solutions to enterprises and to people as well such as Amazon, Google, and Microsoft expanding t...
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The surge of cyberbullying on social media platforms is a major concern in today's digital age, with its prevalence escalating alongside advancements in technology. Thus, devising methods to detect and eliminate c...
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Offensive language is one of the problems that have become increasingly severe along with the rise of the internet and social media usage. This language can be used to attack a person or specific groups. Automatic mod...
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Social media growth was fast because many people used it to express their feelings, share information, and interact with others. With the growth of social media, many researchers are interested in using social media d...
Lip-reading is a method that focuses on the observation and interpretation of lip movements to understand spoken language. Previous studies have exclusively concentrated on a single variation of residual networks (Res...
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