Comprehensive Study on Advanced Network Based Machine Learning Models for Sentiment Analysis
B.Tulasi Kumari1, D. Rajeswara Rao2
1B.Tulasi Kumari, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation Guntur, Andhra Pradesh, India.
2D. Rajeswara Rao, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation Guntur, Andhra Pradesh, India.
Manuscript received on 05 April 2019 | Revised Manuscript received on 14 April 2019 | Manuscript Published on 24 May 2019 | PP: 312-316 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F10640486S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sentiment Analytics is an inseparable part of today’s data centred decision making. Various domains like Politics, Share Market, Product Management, and Customer Relationship Management are heavily based on public sentiment and have applicability of Sentiment Analysis. Researchers have developed various machine learning models for this task. In last few years those models have proved their performance. Yet there are problems like multi-class sentiment mapping, short text sentiment analysis which need further attention. In this paper sentiment analysis and allied problems attempted using latest machine learning model, Caps Net, is discussed. Recently Caps Net has emerged as a powerful and efficient model for various domains. Its various variants applied for sentiment analysis and related tasks are studied and analyzed here. Purpose of this work is to highlight the role of Caps Net in the area of sentiment analysis. Also, very brief discussion about Convolutional Neural Networks (CNN) is done as CNN is immediate predecessor of Caps Net. Various latest Caps Net models have outperformed state of the art machine learning models for sentiment analysis.
Keywords: Sentiment Analysis, Opining Mining, Caps Net, Convolutional Neural Networks, CNN.
Scope of the Article: Communication