Evaluation of Feelings of Smart Phone Product Review Using SVM Technique of Sentiment Analysis

Authors: Priyanka Parmar , Sakshi Dubey, Shimna Mohan. K, Mohit Kadwal

DOI Link: https://doi.org/10.22214/ijraset.2023.50082

Abstract

Analysis (SA) is a task of identifying positive and negative opinions, emotion and evaluation in text available over the social networking sites and the world wide web have been gained quite a popularity in the recent years. The analysis serves as an important feedback for further improvement in the offered services and user experiences. Several techniques have been used recently including machine learning approaches and vocabulary orientated semantic algorithms. This report presents a survey of various techniques and tools have been used in the previous research sentiment analysis process. There is a massive increase in number of people who access various social networking and micro-blogging websites that gives new shapes the impression of today’s generation. Many reviews for a specific product, brand, individual, and movies etc. are helpful in directing the perception of people thus the analysts are begun to create algorithms to automate the classification of distinctive reviews on the basis of their polarities in particular : Positive, Negative and Neutral. This machine-driven classification mechanism is referred as Sentiment Analysis. The ultimate aim of this paper is to use support vector machine (SVM) classification technique to classify the feelings of good phone product review that analyses datasets used for classification of sentiments and texts.. Also, data sets are used for training as well as testing and implemented through SVM technique for finding the polarity of the ambiguous tweets. The obtain results show to achieve high accuracy as predicted on the basis of reviews of smart phone.

Introduction

Conclusion

This chapter is that the concluding a part of the dissertation work and also proposes some suggestions towards which this work are often further extended. Section 4.1 brings out the general conclusions of the research work administered during this thesis. Section 4.2 gives the longer term research directions and possible extensions of the work presented with thesis.Sentiment analysis is one among the recent research area now a days. the knowledge gathered from the info sources like blogs, forums, review sites etc. has been playing a crucial role in expressing people’s feelings, thoughts, emotions, and opinions for the actual issue or topic. The proposed show works on gathering of tweets identified with smart phone reviews. The exactness has enhanced in differentiation to the various mixes of models utilized by scientists already. The outcomes produce 90.99 % accuracy. Therefore, it are often derived that sentiment analysis is improved by using Support Vector Machine (svm). It work well with predefined quite sentence which we\'ve indicated.

References

[1] Ebru Aydo?gan and M Ali Akcayol. A comprehensive survey for sentiment analysis tasks using machine learning techniques. In INnovations in Intelligent SysTems and Applications (INISTA), 2016 International Symposium on, pages 1–7. IEEE,2016. [2] Abinash Tripathy, Ankit Agrawal, and Santanu Kumar Rath. Classification of sen-timent reviews using n-gram machine learning approach. Expert Systems with Ap-plications, 57:117–126,2016. [3] S Brindha, K Prabha, and S Sukumaran. A survey on classification techniques for text mining. In Advanced Computing and Communication Systems (ICACCS), 2016 3rd International Conference on, volume 1, pages 1–5. IEEE, 2016. [4] Fang Luo, Cheng Li, and Zehui Cao. Affective-feature-based sentiment analysis usingsvm classifier. In Computer Supported Cooperative add Design (CSCWD), 2016 IEEE 20th International Conference on, pages 276–281. IEEE,2016. [5] Mohammed Qasem, RuppaThulasiram, and ParimalaThulasiram. Twitter sentiment classification using machine learning techniques for stock markets. In Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on, pages 834–840. IEEE,2015. [6] M Trupthi, Suresh Pabboju, and G Narasimha. Improved feature extraction and classification sentiment analysis. In Advances in Human Machine Interaction (HMI), 2016 International Conference on, pages 1–6. IEEE, 2016. [7] Orestes Appel, Francisco Chiclana, Jenny Carter, and Hamido Fujita. A hybrid approach to sentiment analysis.2016. [8] Huang Zou, Xinhua Tang, Bin Xie, and Bing Liu. Sentiment classification using machine learning techniques with syntax features. In Computational Science and Computational Intelligence (CSCI), 2015 International Conference on, pages 175– 179. IEEE,2015. [9] P Kalaivani and KL Shunmuganathan. An improved k-nearest-neighbor algorithm using genetic algorithm for sentiment classification. In Circuit, Power and Computing Technologies (ICCPCT), 2014 International Conference on, pages 1647–1651. IEEE, 2014. [10] Mostafa Karamibekr and Ali AGhorbani. A structure for opinion in social domains. In Social Computing (SocialCom), 2013 International Conference on, pages 264–271. IEEE,2013. [11] Hassan Saif, Yulan He, and Harith Alani. Semantic sentiment analysis of twitter. In International Semantic Web Conference, pages 508–524. Springer,2012. [12] Alexander Pak and Patrick Paroubek. Twitter as a corpus for sentiment analysis and opinion mining. In LREc, volume 10, pages 1320–1326, 2010. [13] Khin Phyu PhyuShein andThiThi Soe Nyunt. Sentiment classification supported ontology and svm classifier. In Communication Software and Networks, 2010. ICCSN\'10. Second International Conference on, pages 169–172. IEEE,2010. [14] Yueheng Sun, Linmei Wang, and Zheng Deng. Automatic sentiment analysis for web user reviews. In informatics and Engineering (ICISE), 2009 1st Inter-national Conference on, pages 806–809. IEEE,2009. [15] Cheng Mingzhi, Xin Yang, Bao Jingbing, Wang Cong, and Yang Yixian. A stochastic process method for sentiment classification. In Future Information Technology and Management Engineering, 2009. FITME\'09. Second International Conference on, pages 327–330. IEEE,2009. [16] Mostafa Al Masum Shaikh, Helmut Prendinger, and Mitsuru Ishizuka. An analytical approach to assess sentiment of text. In Computer and knowledge technology, 2007. iccit 2007. 10th international conference on, pages 1–6. IEEE, 2007.

Copyright

Copyright © 2023 Priyanka Parmar , Sakshi Dubey, Shimna Mohan. K, Mohit Kadwal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Paper Id : IJRASET50082

Publish Date : 2023-04-04

ISSN : 2321-9653

Publisher Name : IJRASET

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