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Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model

Procedia computer science 2023年 227卷 168-175页

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app; there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80.

关键词： Sentiment Analysis TikTok SVM VADER Sentiment Google Play Store

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A Design of Polygenic Risk Model with Deep Learning for Colorectal Cancer in Multiethnic Indonesians

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Procedia computer science 2021年 179卷 632-639页

作者： Steven Amadeus Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Recently, health management is emerging and attract attention to how to provide better prognostication and health management systems. The challenges in the prognostication are how to develop a model that can self-learn the prognostication features and how to get a high accuracy prediction. Prognostication in health disease involves SNPs which is a genetic marker. In this paper, we propose a polygenic risk model using deep learning: Transformer with self-attention mechanism and DeepLIFT. The use of these deep learning model allows us to predict the risk of colorectal cancer and see the correlation between SNPs.

关键词： Prognostication Polygenic Risk Model Transformer Model DeepLIFT Colorectal Cancer

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Machine Learning Approaches in Detecting Autism Spectrum Disorder

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Procedia computer science 2023年 227卷 1070-1076页

作者： Daniel Nicholas Dominic Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classifi-cation between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD.

关键词： autism spectrum disorder brain atlas brain region machine learning

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Opportunistic spectrum access: Does maximizing throughput minimize file transfer time?

arXiv

引用

arXiv 2021年

作者： Hu, Jie Doshi, Vishwaraj Eun, Do Young The Department of Electrical and Computer Engineering The Operations Research Graduate Program North Carolina State University RaleighNC United States

The Opportunistic Spectrum Access (OSA) model has been developed for the secondary users (SUs) to exploit the stochastic dynamics of licensed channels for file transfer in an opportunistic manner. Common approaches to design channel sensing strategies for throughput-oriented applications tend to maximize the long-term throughput, with the hope that it provides reduced file transfer time as well. In this paper, we show that this is not correct in general, especially for small files. Unlike prior delay-related works that seldom consider the heterogeneous channel rate and bursty incoming packets, our work explicitly considers minimizing the file transfer time of a single file consisting of multiple packets in a set of heterogeneous channels. We formulate a mathematical framework for the static policy, and extend to dynamic policy by mapping our file transfer problem to the stochastic shortest path problem. We analyze the performance of our proposed static optimal and dynamic optimal policies over the policy that maximizes long-term throughput. We then propose a heuristic policy that takes into account the performance-complexity tradeoff and an extension to online implementation with unknown channel parameters, and also present the regret bound for our online algorithm. We also present numerical simulations that reflect our analytical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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Terahertz Cavity Phonon Polaritons in Lead Telluride in the Deep-Strong Coupling Regime

arXiv

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arXiv 2025年

作者： Baydin, Andrey Manjappa, Manukumara Mishra, Sobhan Subhra Xu, Hongjing Doumani, Jacques Tay, Fuyang Kim, Dasom Rappl, Paulo H.O. Abramof, Eduardo Singh, Ranjan Hernandez, Felix G.G. Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley–Curl Institute Rice University HoustonTX77005 United States Rice Advanced Materials Institute Rice University HoustonTX77005 United States Instrumentation and Applied Physics Department Indian Institute of Science Bangalore560012 India Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University 639798 Singapore Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX77005 United States Group of Research and Development in Materials and Plasma National Institute for Space Research São José dos Campos São Paulo12201-970 Brazil Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Instituto de Física Universidade de São Paulo São Paulo05508-090 Brazil Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

Lead telluride is an important thermoelectric material due to its large Seebeck coefficient combined with its unusually low thermal conductivity that is related to the strong anharmonicity of phonons in this material. Here, we have studied the resonant and nonperturbative coupling of transverse optical phonons in lead telluride with cavity photons inside small-mode-volume metallic metasurface cavities that have photonic modes with terahertz frequencies. We observed a giant vacuum Rabi splitting on the order of the bare phonon and cavity frequencies. Through terahertz time-domain spectroscopy experiments, we systematically studied the vacuum Rabi splitting as a function of sample thickness, temperature, and cavity length. Under the strongest light–matter coupling conditions, the strength of coupling exceeded the bare phonon and cavity frequencies, putting the system into the deep-strong coupling regime. These results demonstrate that this uniquely tunable platform is promising for realizing and understanding predicted cavity-vacuum-induced ferroelectric instabilities and exploring applications of light–matter coupling in the ultrastrong and deep-strong coupling regimes in quantum technology. Copyright © 2025, The Authors. All rights reserved.

关键词： Phonons

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Using memory access traces to map threads and data on hierarchical multi-core platforms

Using memory access traces to map threads and data on hierar...

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25th IEEE International Parallel and Distributed Processing Symposium, Workshops and Phd Forum, IPDPSW 2011

作者： Da Cruz, Eduardo Henrique Molina Alves, Marco Antonio Zanata Carissimi, Alexandre Navaux, Philippe Olivier Alexandre Ribeiro, Christiane Pousa Méhaut, Jean-François PPGC Graduate Program in Computer Science Institute of Informatics UFRGS Federal University of Rio Grande do Sul Porto Alegre RS Brazil INRIA Mescal Research Team LIG Laboratory Grenoble University France

ISBN: (纸本)9780769543857

In parallel programs, the tasks of a given application must cooperate in order to accomplish the required computation. However, the communication time between the tasks may be different depending on which core they are executing and how the memory hierarchy and interconnection are used. The problem is even more important in multi-core machines with NUMA characteristics, since the remote access imposes high overhead, making them more sensitive to thread and data mapping. In this context, process mapping is a technique that provides performance gains by improving the use of resources such as interconnections, main memory and cache memory. The problem of detecting the best mapping is considered NP-Hard. Furthermore, in shared memory environments, there is an additional difficulty of finding the communication pattern, which is implicit and occurs through memory accesses. This work aims to provide a method for static mapping for NUMA architectures which does not require any prior knowledge of the application. Different metrics were adopted and an heuristic method based on the Edmonds matching algorithm was used to obtain the mapping. In order to evaluate our proposal, we use the NAS Parallel Benchmarks (NPB) and two modern multi-core NUMA machines. Results show performance gains of up to 75% compared to the native scheduler and memory allocator of the operating system. © 2011 IEEE.

关键词： Cache memory

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS 15

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BAS...

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15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on computer science and Information Systems, MCCSIS 2022

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. dos Coelho, Leandro S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

ISBN: (纸本)9789898704405

Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network’s performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models’ performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals’ data. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on computer science and Information Systems, MCCSIS 2022. All rights reserved.

关键词： Neural networks

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Transfer Learning from Chest X-Ray Pre-trained Convolutional Neural Network for Learning Mammogram Data

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Procedia computer science 2018年 135卷 400-407页

作者： Bens Pardamean Tjeng Wawan Cenggoro Reza Rahutomo Arif Budiarto Ettikan Kandasamy Karuppiah Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 NVIDIA Corporation

Breast cancer is one of the deadliest cancer for female nowadays. Despite of the rapid advancement in medical image analysis with the rise of deep learning, development of breast cancer detection system is limited due to relatively small size of the publicly available mammogram dataset. In this paper, we discover an effective configuration for transfer learning from Chest X-Ray pre-trained Convolutional Neural Network to overcome the small-size mammogram dataset problem. We found that the best configuration achieve 90.38% validation accuracy for modified.

关键词： Transfer learning Convolutional neural network Medical image analysis Chest X-ray Mammogram

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Extreme electrodynamics in time-varying media

arXiv

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arXiv 2025年

作者： Scalora, M. Vincenti, M.A. de Ceglia, D. Akozbek, N. Ferrera, M. Rizza, C. Alù, A. Litchinitser, N. Cojocaru, C. Trull, J. FCDD-AMT-MGR DEVCOM AvMC C. M. Bowden Research Center Redstone ArsenalAL35898-5000 United States Department of Information Engineering University of Brescia Brescia25123 Italy US Army Space & Missile Defense Command Tech Center Redstone ArsenalAL35898 United States Institute of Photonics and Quantum Sciences Heriot-Watt University SUPA EdinburghEH14 4AS United Kingdom Department of Physical and Chemical Sciences University of L’Aquila L’AquilaI-67100 Italy Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Universitat Politècnica de Catalunya Barcelona Terrassa08222 Spain

Abrupt time variations of the properties of optical materials have been at the center of intense research efforts in recent years, with the prospect of enabling extreme wave transformations and of leveraging time as a degree of freedom for wave control. While the most viable approach to yield ultrafast variations of the optical material response is through optical pumping of nonlinear media, the complex dynamics in these systems are not yet fully understood. Here, as a relevant case study, we rigorously investigate the pump-probe dynamics in a 310nm-thick transparent conductive oxide etalon, using a weak 40 femtosecond probe and a pump that displays peak power densities in the TW/cm2 range with a duration of a few femtoseconds. We examine the pump-probe interaction using a hydrodynamic-Maxwell approach that accounts for diffraction, self-focusing and -defocusing, self- and cross-phase modulation, probe gain, and linear and nonlinear material dispersion expanded in the perturbative regime up to 9th order for both pump and probe. By allowing the intricacies of the pump-probe interaction to proceed in time, we can also define an effective spatio-temporal permittivity for a more direct evaluation of the material ultra-broadband optical behavior. The reported results challenge the conventional modeling of this kind of problem, which has so far overlooked pump dynamics, simplistically assigning a local time-dependent refractive index to the probe that may be designed to fit the experimental data, but has no physical connection to the complex pump-probe interaction. Our approach unveils new dynamics, pointing towards the possibility to achieve extreme pulse compression into the attosecond range and nonlinear diffraction over deeply subwavelength propagation distances, thus opening a possible new path towards novel and cost-effective tools for integrated photonics and attosecond science. © 2025, CC BY-NC-ND.

关键词： Probes

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