Aspect-Based Sentiment Analysis Using Smart Company and Hotel Aspect Review Data

Authors

  • C. Selvi  Department of CSE, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India
  • Niveda. C. P  PG Scholar, Department of CSE, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India

DOI:

https://doi.org//10.32628/IJSRST207223

Keywords:

Aspect frequency, aspect ranking, language processing technique, aspect-sentiment based embedding.

Abstract

Digital sources such as smart applications opinions and online feedback statistics are crucial resources to be seeking for customers’ remarks and input. However, the reviews are often disorganized, leading to difficulties in information navigation and knowledge acquisition. The aforementioned problem is overcome by generating aspect-sentiment based embedding for the hotels and companies by looking into reliable reviews of them. The important product aspects are identified based on two observations: 1) the important aspects are usually commented on by a large number of consumers and 2) consumer opinions on the important aspects greatly influence their overall opinions. Aspect frequency and the influence of consumer opinions given to each aspect over their overall opinions are identified for hotel reviews whereas for company reviews approach adopts language processing techniques, policies, and lexicons to address several sentiment evaluation challenges, and convey summarized results. Moreover, aspect ranking achieve significant performance improvements, which demonstrate the capacity of aspect ranking in facilitating real-world applications.

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Published

2020-04-30

Issue

Section

Research Articles

How to Cite

[1]
C. Selvi, Niveda. C. P, " Aspect-Based Sentiment Analysis Using Smart Company and Hotel Aspect Review Data, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 7, Issue 2, pp.112-118, March-April-2020. Available at doi : https://doi.org/10.32628/IJSRST207223