Aspect-oriented analytics of big data

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Social media platforms are one of the most significant contributors to big data; it enables consumers to provide their views or opinions about products and services. These abundant reviews contain substantial and valuable knowledge and have become a significant resource for both consumers and firms. Therefore, enterprises seek realtime insights and relevant information on how the market responds to products and services. The proposed framework employs the sentiment analysis and aspect-based sentiment analysis in parallel to customer reviews to support decision-makers regarding Marketing and Manufacturing domains. Our proposal presents a multilayer classifier for consumers' reviews. The first layer is used to categorize reviews into the aspect and non-aspect classes. The second layer is used to break every review involved in the aspect-based category into opinion units based on the product aspects. Next, we plan to measure the polarity of the reviews and opinion units. Finally, we plan to visualize the results in the form of domain-oriented reports. Also, we present a description of the testing and evaluation criteria.

Original languageEnglish
Pages (from-to)41-48
Number of pages8
JournalCEUR Workshop Proceedings
StatePublished - 1 Jan 2020
Event14th Joint International Baltic Conference on Databases and Information Systems Forum and Doctoral Consortium, Baltic-DB and IS-Forum-DC 2020 - Tallinn, Estonia
Duration: 16 Jun 202019 Jun 2020

Scopus subject areas

  • Computer Science(all)


  • Aspect-based sentiment analysis
  • Big data analytics
  • Decision making
  • Sentiment analysis


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