Skip to content
Research Article Open access CC BY 4.0

Unsupervised Fuzzy-Multi-Core Aspect Sentiment Analysis for Big Data of Online News Users' Persian Opinions

Seyed Omid Azarkasb, Mostafa Amiri, Seyed Hossein Khasteh

Asian Journal of Research in Computer Science · pp. 50–64 · Published 3 Jul 2023

10.9734/ajrcos/2023/v16i3345

Abstract

An online news article can cover various topics or contain different aspects of a subject, encouraging readers to express their opinions on specific topics or aspects. Sentiment analysis evaluates the overall sentiment of the audience towards the entire news article, whether it is positive, negative, or neutral. However, in aspect-based sentiment analysis, the focus is on determining which aspect of the news article the opinion is referring to. Extracting the relevant aspect in sentiment analysis involves identifying the part of the article that the reader has expressed an opinion about. This task can lead to a more precise analysis of audience reactions to future news and events. To accomplish this, the news text is segmented into constituent sentences and transformed into a vector space. Then, an unsupervised clustering method is applied to extract various aspects of the news. Fuzzy multi-core clustering is employed as the clustering technique, which has lower computational overhead and can handle uncertain, noisy, and outlier data easily. The implemented approach is based on the concept of feasibility and utilizes multi-core learning to detect clusters in complex data structures. This method remains robust against issues such as ineffective cores or unrelated features by automatically adjusting the core weights within an optimized framework. Furthermore, support vector machines are employed to establish the relationship between opinions and relevant aspects. The transition to the vector space, the mapping process, clustering operations, and aspect extraction are performed in the reducer.

Aspect-based sentiment analysis fuzzy multi-core clustering opinion mining Persian online news big data

References (29)

  1. 1 Sentiment Analysis and Opinion Mining [DOI]
  2. 2 A comprehensive survey on sentiment analysis: Approaches, challenges and trends [DOI]
  3. 3 Sentiment classification of online reviews to travel destinations by supervised machine learning approaches [DOI]
  4. 4 Sentiment analysis based on improved pre-trained word embeddings [DOI]
  5. 5 Mining comparative opinions from customer reviews for Competitive Intelligence [DOI]
  6. 6 Transformer based Deep Intelligent Contextual Embedding for Twitter sentiment analysis [DOI]
  7. 7 Using VADER sentiment and SVM for predicting customer response sentiment [DOI]
  8. 8 Sentiment analysis: A survey on design framework, applications and future scopes [DOI]
  9. 9 Enhanced news sentiment analysis using deep learning methods [DOI]
  10. 10 Data clustering with size constraints [DOI]
  11. 11 Fuzzy logic applied to opinion mining: A review [DOI]
  12. 12 Opinion mining based on fuzzy domain ontology and Support Vector Machine: A proposal to automate online review classification [DOI]
  13. 13 Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes [DOI]
  14. 14 Sentiment based matrix factorization with reliability for recommendation [DOI]
  15. 15 KSCB: a novel unsupervised method for text sentiment analysis [DOI]
  16. 16 Multiple Kernel k-Means with Incomplete Kernels [DOI]
  17. 17 Active constrained fuzzy clustering: A multiple kernels learning approach [DOI]
  18. 18 Cross-lingual alignments of ELMo contextual embeddings [DOI]
  19. 19 Joint Aspect-Sentiment Analysis with Minimal User Guidance [DOI]
  20. 20 A kernel semi-supervised distance metric learning with relative distance: Integration with a MOO approach [DOI]
  21. 21 Opinion Mining Using Multi-Dimensional Analysis [DOI]
  22. 22 Opinion Mining of Consumer Reviews Using Deep Neural Networks with Word-Sentiment Associations [DOI]
  23. 23 Robust Kernel Clustering Algorithm for Nonlinear System Identification [DOI]
  24. 24 Fuzzy Entropy Clustering Using Possibilistic Approach [DOI]
  25. 25 Opinion Mining and Review Spam Detection: Issues and Challenges [DOI]
  26. 26 Efficient Kernel Learning from Constraints and Unlabeled Data [DOI]
  27. 27 AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale [DOI]
  28. 28 Sentiment Analysis and Opinion Mining [DOI]
  29. 29 A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter [DOI]

Cited by 2

2 citations reported by external sources — individual citing-article records aren't available to list yet.

Related research

Article metrics

Real usage data collected on this platform.

8

Page views

0

PDF downloads

0

Outbound clicks

2

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.