Abstract
Multilingual and multimodal data analysis is the emerging news feed evaluation system. News feed analysis and evaluations are interrelated processes, which are useful in understanding the news factors. The news feed evaluation system can be implemented for single or multilingual language models. Classification techniques used on multilingual news analysis require deep layered learning techniques rather than conventional approaches. In this proposed work, a hierarchical structure of deep learning algorithms is implemented for making an effective complex news evaluation system. Deep learning techniques such as the Deep Cooperative Multilingual Reinforcement Learning Model, the Multidimensional Genetic Algorithm, and the Multilingual Generative Adversarial Network are developed to evaluate a vast number of news feeds. The proposed tech-niques collaborate in a pipeline order to build a deep news feed evaluation system. The implementation details project that the newly proposed system performs 5% to 12% better than the other news evaluation systems.
- Morteza Zihayat, Anteneh Ayanso, Xing Zhao, Heidar Davoudi, and Aijun An. 2019. A utility-based news recommendation system. Decision Support Systems 117 (2019), 14--27.Google Scholar
Cross Ref
- Omar A. Usman, Asad A. Usman, and Michael A. Ward. 2019. Comparison of SIRS, qSOFA, and NEWS for the early identification of sepsis in the emergency department. American Journal of Emergency Medicine 37, 8 (2019), 1490--1497.Google Scholar
Cross Ref
- Kristoffer Holt, Tine Ustad Figenschou, and Lena Frischlich. 2019. Key dimensions of alternative news media. Digital Journalism 7, 7 (2019), 860--869.Google Scholar
Cross Ref
- Christopher Soo-Guan Khoo, Armineh Nourbakhsh, and Jin-Cheon Na. 2012. Sentiment analysis of online news text: A case study of appraisal theory. Online Information Review 36, 6 (2012), 858--878.Google Scholar
Cross Ref
- Qurat Tul Ain, Mubashir Ali, Amna Riaz, Amna Noureen, Muhammad Kamran, Babar Hayat, and A. Rehman. 2017. Sentiment analysis using deep learning techniques: A review. International Journal of Advanced Computer Science and Applications 8, 6 (2017), 424.Google Scholar
- Duyu Tang, Bing Qin, and Ting Liu. 2015. Deep learning for sentiment analysis: Successful approaches and future challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5, 6 (2015), 292--303.Google Scholar
Digital Library
- Hai Ha Do, P. W. C. Prasad, Angelika Maag, and Abeer Alsadoon. 2019. Deep learning for aspect-based sentiment analysis: A comparative review. Expert Systems with Applications 118 (2019), 272--299.Google Scholar
Digital Library
- Sujata Rani and Parteek Kumar. 2019. Deep learning based sentiment analysis using convolution neural network. Arabian Journal for Science and Engineering 44, 4 (2019), 3305--3314.Google Scholar
Cross Ref
- Iti Chaturvedi, Ranjan Satapathy, Sandro Cavallari, and Erik Cambria. 2019. Fuzzy commonsense reasoning for multimodal sentiment analysis. Pattern Recognition Letters 125 (2019), 264--270.Google Scholar
Cross Ref
- Johannes Kiesel, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, Payam Adineh, David Corney, Benno Stein, and Martin Potthast. 2019. Semeval-2019 task 4: Hyperpartisan news detection. In Proceedings of the 13th International Workshop on Semantic Evaluation. 829--839.Google Scholar
Cross Ref
- Wataru Souma, Irena Vodenska, and Hideaki Aoyama. 2019. Enhanced news sentiment analysis using deep learning methods. Journal of Computational Social Science 2, 1 (2019), 33--46.Google Scholar
Cross Ref
- Jurgita Kapočiūtė-Dzikienė, Robertas Damaševičius, and Marcin Woźniak. 2019. Sentiment analysis of Lithuanian texts using traditional and deep learning approaches. Computers 8, 1 (2019), 4.Google Scholar
Cross Ref
- Monika Arora and Vineet Kansal. 2019. Character level embedding with deep convolutional neural network for text normalization of unstructured data for twitter sentiment analysis. Social Network Analysis and Mining 9, 1 (2019), 12.Google Scholar
Cross Ref
- Oscar Araque, Ignacio Corcuera-Platas, J. Fernando Sánchez-Rada, and Carlos A. Iglesias. 2017. Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications 77 (2017), 236--246.Google Scholar
Digital Library
- Marcos Pastorini, Mauricio Pereira, Nicolás Zeballos, Luis Chiruzzo, Aiala Rosá, and Mathias Etcheverry. 2019. RETUYT-InCo at TASS 2019: Sentiment analysis in Spanish tweets. In Proceedings of the 30th Teaching Academic Survival and Success Conference.Google Scholar
- Lutfiye Seda Mut Altin, Alex Bravo, and Horacio Saggion. 2019. LaSTUS/TALN at TASS 2019: Sentiment analysis for Spanish language variants with neural networks. In Proceedings of the 30th Teaching Academic Survival and Success Conference.Google Scholar
- H. Sankar, V. Subramaniyaswamy, V. Vijayakumar, Sangaiah Arun Kumar, R. Logesh, and A. Umamakeswari. 2019. Intelligent sentiment analysis approach using edge computing-based deep learning technique. Software: Practice and Experience 50, 5 (2019), 645--657.Google Scholar
Cross Ref
- Pradeepthi Nimirthi, P. Venkata Krishna, Mohammad S. Obaidat, and V. Saritha. 2019. A framework for sentiment analysis based recommender system for agriculture using deep learning approach. In Social Network Forensics, Cyber Security, and Machine Learning. Springer, Singapore, 59--66.Google Scholar
- Syeda Rida-E-Fatima, Ali Javed, Ameen Banjar, Aun Irtaza, Hassan Dawood, Hussain Dawood, and Abdullah Alamri. 2019. A multi-layer dual attention deep learning model with refined word embeddings for aspect-based sentiment analysis. IEEE Access 7 (2019), 114795--114807.Google Scholar
- Connor Shorten and Taghi M. Khoshgoftaar. 2019. A survey on image data augmentation for deep learning. Journal of Big Data 6, 1 (2019), 60.Google Scholar
Cross Ref
- Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, and Matti Pietikäinen. 2020. Deep learning for generic object detection: A survey. International Journal of Computer Vision 128, 2 (2020), 261--318.Google Scholar
Digital Library
- Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, and Hamed Haddadi. 2020. A hybrid deep learning architecture for privacy-preserving mobile analytics. arXiv:1703.02952Google Scholar
- Hongwei Wang, Fuzheng Zhang, Xing Xie, and MinyiGuo. 2018. DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the 2018 World Wide Web Conference. 1835--1844.Google Scholar
Digital Library
- Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, and Tie-Yan Liu. 2018. Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 261--269.Google Scholar
Digital Library
- Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis. 2018. Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience 2018, Article 7068349.Google Scholar
- F. Ullah, S. Jabbar, and F. Al-Turjman. 2020. Programmers’ de-anonymization using a hybrid approach of abstract syntax tree and deep learning. Technological Forecasting and Social Change 159 (2020), 120186.Google Scholar
Cross Ref
- F. Al-Turjman and I. Baali. 2019. Machine learning for wearable IoT-based applications: A survey. Transactions on Emerging Telecommunications Technologies. Published online May 16, 2019.Google Scholar
Cross Ref
Index Terms
Multimodal News Feed Evaluation System with Deep Reinforcement Learning Approaches
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