Sentiment analysis is a process of dealing with people's opinions, remarks, and comments to extract valuable insights from them. Sentiment analysis can be used for various purposes like market analysis, campaign m...
详细信息
Sentiment analysis is a process of dealing with people's opinions, remarks, and comments to extract valuable insights from them. Sentiment analysis can be used for various purposes like market analysis, campaign monitoring, decision-making, etc. In recent years, there has been much research on sentiment classification, particularly in English. However, these existing approaches used for the English language cannot be applied to the Urdu language. The substantial rise in communication traffic, including audio, text, video, and pictures, has significantly shifted the Internet of Things (IoT) from scalar to Multimedia Internet of Things (MIoT). So far, the integration of MIoT and NLP systems has received less attention, but it has evolved as a novel research paradigm for smart applications. This article proposes deep learning techniques for sentence-level Urdu sentiment analysis (Urdu SA) for MIoT. Our approach consists of various phases, i.e., data gathering, text preprocessing, model training, testing, and evaluation. A data set of 25 thousand Urdu reviews are used for training the proposed models. This data set is built by scraping various Urdu blogs and social media platforms, and some part of the IMDB data set is used after translating it into the Urdu language. Native Urdu speakers do data annotation, and various preprocessing techniques, i.e., tokenization, stemming, etc., are applied. The two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM), are trained on preprocessed Urdu reviews to find their sentiments in this article. Both models are tested using various combinations of hyperparameters, and each model's accuracy and F1 scores are evaluated. The study results show that the LSTM model outperforms the CNN model by achieving a 96% accuracy and 91% F1 score.
Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data manag...
详细信息
Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.
This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses st...
详细信息
ISBN:
(数字)9789813299498
ISBN:
(纸本)9789813299481
This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
暂无评论