An Aspect-Sentiment Pair Extraction Approach Based on Latent Dirichlet Allocation

Authors

DOI:

https://doi.org/10.18201/ijisae.2018644779

Keywords:

Aspect-Sentiment Pair Extraction, Latent Dirichlet Allocation (LDA), Sentiment Analysis, Turkish User Reviews

Abstract

Online user reviews have a great influence on decision-making process of customers and product sales of companies. However, it is very difficult to obtain user sentiments among huge volume of data on the web consequently; sentiment analysis has gained great importance in terms of analyzing data automatically. On the other hand, sentiment analysis divides itself into branches and can be performed better with aspect level analysis. In this paper, we proposed to extract aspect-sentiment pairs from a Turkish reviews dataset. The proposed task is the fundamental and indeed the critical step of the aspect level sentiment analysis. While extracting aspect-sentiment pairs, an unsupervised topic model Latent Dirichlet Allocation (LDA) is used. With LDA, aspect-sentiment pairs from user reviews are extracted with 0.86 average precision based on ranked list. The aspect-sentiment pair extraction problem is first time realized with LDA on a real-world Turkish user reviews dataset. The experimental results show that LDA is effective and robust in aspect-sentiment pair extraction from user reviews.

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Author Biographies

Ekin Ekinci, Kocaeli University

Faculty of Engineering, Computer Engineering Department

Sevinc Ilhan Omurca, Kocaeli University

Faculty of Engineering, Computer Engineering Department

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Published

26.09.2018

How to Cite

Ekinci, E., & Ilhan Omurca, S. (2018). An Aspect-Sentiment Pair Extraction Approach Based on Latent Dirichlet Allocation. International Journal of Intelligent Systems and Applications in Engineering, 6(3), 209–213. https://doi.org/10.18201/ijisae.2018644779

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Section

Research Article