RETRACTED ARTICLE: Sentiment classification using harmony random forest and harmony gradient boosting machine

被引：0
|
作者：
K. Sridharan
G. Komarasamy
机构：
[1] Anna University,
[2] Bannari Amman Institute of Technology,undefined
来源：
Soft Computing | 2020年 / 24卷
关键词：
Sentiment analysis (SA); Harmony search (HS) algorithm; Gradient boosting and random forest (RF);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The building of a system for exploring the opinions of users that are made in the blog posts, tweets, reviews or comments regarding a particular topic, policy or a product is known as sentiment analysis. The primary aim of this is the determination of the user attitude regarding a certain topic. The harmony search algorithm has proved to be extremely useful in a varied range of problems in optimization. This shows better performance compared to the other techniques of optimization. Another very powerful technique that is applied to machine learning which is now getting extremely popular is gradient boosting. There are several tree parameters which have been optimized for the random forest and the gradient boosting machine that make use of the harmony search algorithm.
引用
收藏
页码：7451 / 7458
页数：7
相关论文
共 50 条
  • [21] RETRACTED: Classification and Prediction of Software Incidents Using Machine Learning Techniques (Retracted Article)
    Ali, Sikandar
    Adeel, Muhammad
    Johar, Sumaira
    Zeeshan, Muhammad
    Baseer, Samad
    Irshad, Azeem
    SECURITY AND COMMUNICATION NETWORKS, 2021, 2021
  • [22] Classification of Dementia Using Harmony Search Optimization Technique
    Bharanidharan, N.
    Rajaguru, Harikumar
    2018 IEEE REGION 10 HUMANITARIAN TECHNOLOGY CONFERENCE (R10-HTC), 2018,
  • [23] RETRACTED ARTICLE: Classification of stroke disease using machine learning algorithms
    Priya Govindarajan
    Ravichandran Kattur Soundarapandian
    Amir H. Gandomi
    Rizwan Patan
    Premaladha Jayaraman
    Ramachandran Manikandan
    Neural Computing and Applications, 2020, 32 : 817 - 828
  • [24] An Ensemble of Random Forest Gradient Boosting Machine and Deep Learning Methods for Stock Price Prediction
    Shrivastav, Lokesh Kumar
    Kumar, Ravinder
    JOURNAL OF INFORMATION TECHNOLOGY RESEARCH, 2022, 15 (01)
  • [25] Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques
    Islam, Samiul
    Amin, Saman Hassanzadeh
    JOURNAL OF BIG DATA, 2020, 7 (01)
  • [26] Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques
    Samiul Islam
    Saman Hassanzadeh Amin
    Journal of Big Data, 7
  • [27] Evaluation of pedotransfer functions to estimate saturated hydraulic conductivity using machine learning with random forest and gradient boosting algorithms
    Mady, Ahmed Y.
    Abdelhamid, Mhamoud A.
    Shalaby, Lina A.
    Saeed, Mohamed A.
    SOIL & ENVIRONMENT, 2024, 43 (02) : 268 - 277
  • [28] RETRACTED: Signal fusion-based deep fast random forest method for machine health assessment (Retracted Article)
    Yuan, Nanqi
    Yang, Wenli
    Kang, Byeong
    Xu, Shuxiang
    Li, Chengjiang
    JOURNAL OF MANUFACTURING SYSTEMS, 2018, 48 : 1 - 8
  • [29] RETRACTED: Identification of Tumor Tissue of Origin with RNA-Seq Data and Using Gradient Boosting Strategy (Retracted Article)
    Li, Ruixi
    Liao, Bo
    Wang, Bo
    Dai, Chan
    Liang, Xin
    Tian, Geng
    Wu, Fangxiang
    BIOMED RESEARCH INTERNATIONAL, 2021, 2021
  • [30] RETRACTED: Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach (Retracted Article)
    Alyas, Tahir
    Hamid, Muhammad
    Alissa, Khalid
    Faiz, Tauqeer
    Tabassum, Nadia
    Ahmad, Aqeel
    BIOMED RESEARCH INTERNATIONAL, 2022, 2022
12345