Analysis on Various Machine Learning based Approaches with a Perspective on the Performance

被引:0
|
作者
Rani, Meesala Shobha [1 ]
Sumathy, S. [2 ]
机构
[1] VIT Univ, Sch Comp Sci & Engn, Vellore, Tamil Nadu, India
[2] VIT Univ, Sch Informat Technol & Engn, Vellore, Tamil Nadu, India
关键词
Machine Learning; Supervised; Semi-supervised; Unsupervised; Sentiment Analysis; SENTIMENT ANALYSIS; FEATURE-SELECTION; TEXT CATEGORIZATION; CLASSIFICATION; REVIEWS; PREDICTION;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the rapid development of web 2.0, increased users of online social media show keen interest to express their opinion and reviews on numerous aspects such as microblogs, various products, hotels, movies and political issues. At present, text mining plays a vital role in various application domains such as online media, healthcare, security applications, business, marketing and industrial applications. In text mining, sentiment analysis or opinion mining is a task carried over to extract or classify the information. This paper presents an exhaustive study on the performance factors highlighting the current state-of art techniques and the open issues on various machine learning based text mining approaches.
引用
收藏
页数:7
相关论文
共 50 条
  • [1] Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans
    Gur Amrit Pal Singh
    P. K. Gupta
    Neural Computing and Applications, 2019, 31 : 6863 - 6877
  • [2] Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans
    Singh, Gur Amrit Pal
    Gupta, P. K.
    NEURAL COMPUTING & APPLICATIONS, 2019, 31 (10): : 6863 - 6877
  • [3] Machine Learning Approaches of Lung and Identifying Various Stages of Analysis
    Harshavardhan, A.
    Babu, D. Suresh
    Senthilkumar, K. P.
    Kannan, L. Mohana
    Sudalaikani, S.
    Rufus, Herald Anantha
    JOURNAL OF ELECTRICAL SYSTEMS, 2024, 20 : 768 - 776
  • [4] Comparative Analysis of Various Machine Learning Approaches for Bitcoin Price Prediction
    Muvvala, Abhishek
    Chivukula, Rohit
    Lakshmi, T. Jaya
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN SIGNAL PROCESSING AND ARTIFICIAL INTELLIGENCE, ASPAI' 2020, 2020, : 161 - 164
  • [5] Prediction of Pipe Performance with Ensemble Machine Learning based Approaches
    Shi, Fang
    Liu, Zheng
    Li, Eric
    2017 INTERNATIONAL CONFERENCE ON SENSING, DIAGNOSTICS, PROGNOSTICS, AND CONTROL (SDPC), 2017, : 408 - 414
  • [6] Comparative Analysis of Classification of Neonatal Bilirubin by Using Various Machine Learning Approaches
    Bhagat, Priti, V
    Raghuwanshi, Mukesh M.
    Bagde, Ashutosh D.
    CUREUS JOURNAL OF MEDICAL SCIENCE, 2024, 16 (06)
  • [7] Comparison of various uncertainty modelling approaches based on geostatistics and machine learning algorithms
    Szatmari, Gabor
    Pasztor, Laszlo
    GEODERMA, 2019, 337 : 1329 - 1340
  • [8] Performance analysis of machine learning based optimized feature selection approaches for breast cancer diagnosis
    Sharma A.
    Mishra P.K.
    International Journal of Information Technology, 2022, 14 (4) : 1949 - 1960
  • [9] Performance Analysis of Chronic Kidney Disease through Machine Learning Approaches
    Emon, Minhaz Uddin
    Imran, Al Mahmud
    Islam, Rakibul
    Keya, Maria Sultana
    Zannat, Raihana
    Ohidujjaman
    PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2021), 2021, : 713 - 719
  • [10] Performance analysis of various machine learning models for membership inference attack
    Karthikeyan, K.
    Padmanaban, K.
    Kavitha, Datchanamoorthy
    Sekhar, Jampani Chandra
    INTERNATIONAL JOURNAL OF SENSOR NETWORKS, 2023, 43 (04) : 232 - 245