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Prediction of High Recommendation Mobile Brands Using Sentiment Analysis

  • Smita BhanapEmail author
  • Seema Kawthekar
Conference paper
  • 34 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1162)

Abstract

体育赛事投注记录digital world is growing very fact every day. it is the very important resource for generation of digital data. technology is taking us to more sophisticated and user-centric applications in order to perform our daily requirements. mainly, usage of social networks and online transactions is more preferred by people which generates huge data in terms of useful insights for future references. people are more comfortable giving their views, ideas, and reviews online instead of offline. these reviews are the source which if used in appropriate manner can help to understand needs of customers and help them in recommending some brands as per their requirements and help them in decision making. in this paper, we are using tweets of various mobile brands as our input. we then use naïve bayes and support-vector machine supervised machine learning algorithms for prediction of sentiments and prediction of highly recommended brands. in our experiment, we got more accurate results for support-vector machine than naïve bayes.

Keywords

Sentiment analysis Naïve Bayes Support-vector machine Prediction 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Dr. Babasaheb Ambedkar Marathwada UniversityAurangabadIndia

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