Sentiment Analysis using Deep Learning in Cloud

被引:10
|
作者
Raza, Muhammad Raheel [1 ]
Hussain, Walayat [2 ]
Tanyildizi, Erkan [3 ]
Varol, Asaf [4 ]
机构
[1] COMSATS Univ, Islamabad, Pakistan
[2] Univ Technol Sydney, Fac Engn & IT, Sch Informat Syst & Modelling, Sydney, NSW 2007, Australia
[3] Firat Univ, Coll Technol, Dept Software Engn, Elazig, Turkey
[4] Maltepe Univ, Fac Engn & Nat Sci, Dept Comp Engn, Istanbul, Turkey
关键词
Deep Learning; Sentiment Analysis; Cloud Computing; POLARITY;
D O I
10.1109/ISDFS52919.2021.9486312
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Sentiments are the emotions or opinions of an individual encapsulated within texts or images. These emotions play a vital role in the decision-making process for a business. A cloud service provider and consumer are bound together in a Service Level Agreement (SLA) in a cloud environment. SLA defines all the rules and regulations for both parties to maintain a good relationship. For a long-lasting and sustainable relationship, it is vital to mine consumers' sentiment to get insight into the business. Sentiment Analysis or Opinion Mining refers to the process of extracting or predicting different point of views from a text or image to conclude. Various techniques, including Machine Learning and Deep Learning, strives to achieve results with high accuracy. However, most of the existing studies could not unveil hidden parameters in text analysis for optimal decision-making. This work discusses the application of sentiment analysis in the cloud-computing paradigm. The paper provides a comparative study of various textual sentiment analysis using different deep learning approaches and their importance in cloud computing. The paper further compares existing approaches to identify and highlight gaps in them.
引用
收藏
页数:5
相关论文
共 50 条
  • [1] Application of Deep Learning to Sentiment Analysis for Recommender System on Cloud
    Preethi, G.
    Krishna, P. Venkata
    Obaidat, Mohammad S.
    Saritha, V.
    Yenduri, Sumanth
    [J]. 2017 INTERNATIONAL CONFERENCE ON COMPUTER, INFORMATION AND TELECOMMUNICATION SYSTEMS (IEEE CITS), 2017, : 93 - 97
  • [2] Sentiment Analysis using Machine Learning and Deep Learning
    Chandra, Yogesh
    Jana, Antoreep
    [J]. PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT (INDIACOM-2020), 2019, : 1 - 4
  • [3] Sentiment Analysis of Tweets Using Deep Learning
    Ranganathan, Jaishree
    Tsahai, Tsega
    [J]. ADVANCED DATA MINING AND APPLICATIONS (ADMA 2022), PT I, 2022, 13725 : 106 - 117
  • [4] Image Sentiment Analysis using Deep Learning
    Mittal, Namita
    Sharma, Divya
    Joshi, Manju Lata
    [J]. 2018 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2018), 2018, : 684 - 687
  • [5] Multimodal Sentiment Analysis Using Deep Learning
    Sharma, Rakhee
    Le Ngoc Tan
    Sadat, Fatiha
    [J]. 2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2018, : 1475 - 1478
  • [6] ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing
    Mohsen Ghorbani
    Mahdi Bahaghighat
    Qin Xin
    Figen Özen
    [J]. Journal of Cloud Computing, 9
  • [7] ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing
    Ghorbani, Mohsen
    Bahaghighat, Mahdi
    Xin, Qin
    Ozen, Figen
    [J]. JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS, 2020, 9 (01):
  • [8] Enhancing Sentiment Analysis Using Hybrid Deep Learning
    Ukaihongsar, Watthana
    Jitsakul, Watchareewan
    [J]. PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON COMPUTING AND INFORMATION TECHNOLOGY (IC2IT 2022), 2022, 453 : 183 - 193
  • [9] Sentiment Analysis in Outdoor Images Using Deep Learning
    Bonasoli, Wyverson
    Dorini, Leyza
    Minetto, Rodrigo
    Silva, Thiago H.
    [J]. WEBMEDIA'18: PROCEEDINGS OF THE 24TH BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB, 2018, : 181 - 188
  • [10] Sentiment analysis using deep learning architectures: a review
    Ashima Yadav
    Dinesh Kumar Vishwakarma
    [J]. Artificial Intelligence Review, 2020, 53 : 4335 - 4385