文献类型： 会议论文
第一作者： Ritu Patidar
作者： Ritu Patidar 1 ; Sachin Patel 1 ;
作者机构： 1.Department of Computer Science Engineering, SAGE University, Indore
关键词： Radio frequency;Sentiment analysis;Machine learning algorithms;Computer architecture;Big Data;Ubiquitous computing;Vectors
会议名称： International Conference on Ubiquitous Computing and Intelligent Information Systems
主办单位：
页码： 251-259
摘要： Opinion mining is the study of user opinions as revealed by their text messages. It comprises categorizing user attitudes into several polarities, such as positive, negative,or neutral. For the analysis, a whole other framework is required, one that can process the enormous volume of data quickly and accurately while maintaining a high levelof unpredictability. More than half of the data produced by e-commerce platforms like Amazon, Flipkart, and others come in the form of text, amounting to 20ZB. These text messages can be carefully analyzed and studied to gaina clear understanding of the thoughts and opinions regarding every part of the business. Over the past ten years, analyzing this enormous amount of data and forecasting user behavior have been the major challenges. To analyze the attitudes, we combine the Hadoop infrastructure with the machine learning technique in this study. With the proposed architecture we will be evaluated certain metricsparameters. It is observed that the random forest achieves better accuracy 96 percent as compared to other machine learning algorithms, hence it works efficiently in Big data environments and achieves maximum accurate results.
分类号： tp393-53
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