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An Efficient Approach to Analyse Sentiment Using Deep Learning

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An Efficient Approach to Analyse Sentiment Using Deep Learni...

作者： Dawn, Indrajit Ganguly, Gopi Bondhu Chakraborty, Koyel Supreme Knowledge Foundation Group of Institutions Department of Computer Science and Engineering West Bengal Mankundu Hooghly India

Recently a huge rise regarding the amount of data in the form of texts is observed and it is equally important to understand the emotion behind these texts. The sentiment analysis of a conversation is very important to judge the mind setup of a person in this era. Sentiment analysis is a technique to explain a view regarding a discussion about a topic either related to verbal or drafted form. It is a very difficult task to analyse the sentiment of a conversation. Deep Learning is the latest process to help analyse near-perfect sentiments. It feeds a lot of data to the algorithm and in the process adjusts itself to constantly progress in the prediction process. In this paper, an attempt has been made to judge the sentiment of a conversation using deep learning techniques. The aim of this work is to establish a model for selecting sentiments from sentences. After the "sentiment" word has been identified, we plan to devise a method that will select a few prior words so that the aspect can also be determined directly from the sentence itself. © 2020, Springer Nature Switzerland AG.

关键词： Sentiment analysis

A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization

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arXiv 2023年

作者： Li, Xiao Zhu, Zhihui Li, Qiuwei Liu, Kai School of Data Science The Chinese University of Hong Kong Shenzhen China China The Department of Electrical and Computer Engineering The University of Denver United States The Decision Intelligence Lab Damo Academy Alibaba Group US United States Computer Science Division Clemson University United States

The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks. Unfortunately, designing fast algorithms for the symmetric NMF is not as easy as for its nonsymmetric counterpart, since the latter admits the splitting property that allows state-of-the-art alternating-type algorithms. To overcome this issue, we first split the decision variable and transform the symmetric NMF to a penalized nonsymmetric one, paving the way for designing efficient alternating-type algorithms. We then show that solving the penalized nonsymmetric reformulation returns a solution to the original symmetric NMF. Moreover, we design a family of alternating-type algorithms and show that they all admit strong convergence guarantee: the generated sequence of iterates is convergent and converges at least sublinearly to a critical point of the original symmetric NMF. Finally, we conduct experiments on both synthetic data and real image clustering to support our theoretical results and demonstrate the performance of the alternating-type algorithms. © 2023, CC BY.

关键词： Matrix algebra

Correction to: Multi-agent-based approach for generation expansion planning in isolated micro-grid with renewable energy sources and battery storage

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The Journal of Supercomputing 2022年第1期79卷 1156-1157页

作者： Paliwal, Priyanka Webber, Julian L. Mehbodniya, Abolfazl Haq, Mohd Anul Kumar, Anil Chaurasiya, Prem Kumar Department of Electrical Engineering MANIT Bhopal India Department of Electronics and Communication Engineering Kuwait College of Science and Technology (KCST) Doha Area Kuwait Department of Computer Science College of Computer and Information Sciences Majmaah University AlMajmaah Saudi Arabia Data Science Research Group School of Computing DIT University Dehradun India Department of Mechanical Engineering Bansal Institute of Science and Technology Bhopal India

In this article the affiliation details for Julian L. Webber were incorrectly assigned.

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On Aggregation in Ensembles of Multilabel Classifiers 23rd

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On Aggregation in Ensembles of Multilabel Classifiers

23rd International Conference on Discovery science, DS 2020

作者： Nguyen, Vu-Linh Hüllermeier, Eyke Rapp, Michael Loza Mencía, Eneldo Fürnkranz, Johannes Heinz Nixdorf Institute and Department of Computer Science Paderborn University Paderborn Germany Knowledge Engineering Group TU Darmstadt Darmstadt Germany Computational Data Analytics Group JKU Linz Linz Austria

ISBN: (纸本)9783030615260

While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far. In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: "predict then combine" (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and "combine then predict" (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation. While both approaches generalize voting techniques commonly used for multilabel ensembles, they allow to explicitly take the target performance measure into account. Therefore, concrete instantiations of CTP and PTC can be tailored to concrete loss functions. Experimentally, we show that standard voting techniques are indeed outperformed by suitable instantiations of CTP and PTC, and provide some evidence that CTP performs well for decomposable loss functions, whereas PTC is the better choice for non-decomposable losses. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

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arXiv 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Mei, Fang Kang, Jiawen Du, Hongyang Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn36830 United States

As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (GAI) to address these issues above and enhance the performance of DRL algorithms in this paper. We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to improve DRL algorithms from the data and policy perspectives. Subsequently, we introduce a framework that demonstrates an actual and novel integration of GAI with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the framework on UAV-assisted integrated near-field/far-field communication to validate the performance of the proposed framework. Moreover, we present several future directions. Finally, the related code is available at: https://***/GAI-enhanced-DRL. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

Ripple Sparse Self-Attention for Monaural Speech Enhancement

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Ripple Sparse Self-Attention for Monaural Speech Enhancement

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qiquan Zhang Hongxu Zhu Qi Song Xinyuan Qian Zhaoheng Ni Haizhou Li School of Electrical Engineering and Telecommunications University of New South Wales Australia Department of Electrical and Computer Engineering National University of Singapore Singapore Alibaba Group China Meta United States Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China

The use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally prohibited for long speech recordings. Moreover, it allows each time frame to attend to all time frames, neglecting the strong local correlations of speech signals. This study presents a simple yet effective sparse self-attention for speech enhancement, called ripple attention, which simultaneously performs fine- and coarse-grained modeling for local and global dependencies, respectively. Specifically, we employ local band attention to enable each frame to attend to its closest neighbor frames in a window at fine granularity, while employing dilated attention outside the window to model the global dependencies at a coarse granularity. We evaluate the efficacy of our ripple attention for speech enhancement on two commonly used training objectives. Extensive experimental results consistently confirm the superior performance of the ripple attention design over standard full self-attention, blockwise attention, and dual-path attention (Sep-Former) in terms of speech quality and intelligibility.

关键词： Training Correlation Computational modeling Speech enhancement Transformer cores Transformers Acoustics

Intelligent Gates Exploring the Massiveness of Random Access Points in NGMA

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Intelligent Gates Exploring the Massiveness of Random Access...

International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sunil Prashanth Kumar P. Aswini H Pal Thethi K Smitha R. Akhilesh Reddy Ali Albawi Department of Mechanical Engineeering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

With the rapid progress of Next-Generation Mobile Architecture come several obstacles in improving user experience and network performance. Active methods frequently face issues in allocating network resources correctly. Consequently this can lead to mismanagement or poor resource utilization affecting how users experience the network. As access requirements increase with the growth of architecture complexity protecting personal data is becoming more challenging. To tackle these issues the Dynamic Access Point Management (DAPM) framework shows great potential as it delivers a creative strategy for resource allocation and network optimization. This model modifies standard resource management practices to become sharper and more adaptable. Thorough examination is needed to confirm DAPM’s superiority over standard methods regarding the resolution of these problems comprehensively. This research will examine the gains in network efficiency and security outcomes from the sophisticated models of DAPM. The detailed methodology seeks to construct a network framework that is both dependable and secure and will boost NGMA’s operational effectiveness and durability.

关键词： data privacy Adaptation models Aerodynamics Dynamic scheduling Throughput User experience Stability analysis Security Resource management Standards

AutoSTL: Automated Spatio-Temporal Multi-Task Learning

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arXiv 2023年

作者： Zhang, Zijian Zhao, Xiangyu Miao, Hao Zhang, Chunxu Zhao, Hongwei Zhang, Junbo College of Computer Science and Technology Jilin University China School of Data Science City University of Hong Kong Hong Kong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Department of Computer Science Aalborg University Denmark JD Intelligent Cities Research China JD iCity JD Technology China Hong Kong Institute for Data Science City University of Hong Kong Hong Kong

Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integrating their inseparable relationship. However, existing studies fail to address this joint learning problem well, which generally solve tasks individually or a fixed task combination. The challenges lie in the tangled relation between different properties, the demand for supporting flexible combinations of tasks and the complex spatio-temporal dependency. To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly. Firstly, we propose a scalable architecture consisting of advanced spatio-temporal operations to exploit the complicated dependency. Shared modules and feature fusion mechanism are incorporated to further capture the intrinsic relationship between tasks. Furthermore, our model automatically allocates the operations and fusion weight. Extensive experiments on benchmark datasets verified that our model achieves state-of-the-art performance. As we can know, AutoSTL is the first automated spatio-temporal multi-task learning method. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart city

Misspelling Semantics in Thai

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arXiv 2022年

作者： Nakwijit, Pakawat Purver, Matthew Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

User-generated content is full of misspellings. Rather than being just random noise, we hypothesise that many misspellings contain hidden semantics that can be leveraged for language understanding tasks. This paper presents a fine-grained annotated corpus of misspelling in Thai, together with an analysis of misspelling intention and its possible semantics to get a better understanding of the misspelling patterns observed in the corpus. In addition, we introduce two approaches to incorporate the semantics of misspelling: Misspelling Average Embedding (MAE) and Misspelling Semantic Tokens (MST). Experiments on a sentiment analysis task confirm our overall hypothesis: additional semantics from misspelling can boost the micro F1 score up to 0.4-2%, while blindly normalising misspelling is harmful and suboptimal. © 2022, CC BY.

关键词： Semantics