Sentiment Miner: A Novel Unsupervised Framework for Aspect Detection from Customer Reviews
Ayoub Bagheri, Shiva Nadi
International Journal of Computational Linguistics Research, Vol. 9, No. 3, 2018.
DOI: https://doi.org/10.6025/jcl/2018/9/3/120-130
Link: http://www.dline.info/jcl/fulltext/v9n3/jclv9n3_3.pdf
Abstract:
With the thriving of review sites on the Web, people have begun to express their opinions on a wide variety of products on several services. Sentiment analysis on entities of products thus becomes a rapid and effective way of assessing
public opinion for business marketing. Sentiment analysis is the computational study of peopleâ€™s opinions, attitude, emotion or appraisal pertaining to topics, objects, products, services, organizations, individuals, events or any attributes of them.
Aspect detection in sentiment analysis helps customers to effectively navigate product information by zooming in on the
product features they are interested in. In this paper we introduce Sentiment Miner, a novel unsupervised framework for
aspect detection problem in a sentiment analysis system for online customer reviews. Sentiment Miner tries to detect aspects
using a bootstrapping algorithm based on pointwise mutual information measure. The proposed framework considers multiword
aspects as atomic terms by utilizing C-value method. Experimental results show that the Sentiment Miner improves the
precision, recall and the F-score, and outperforms the state-of-the-art baseline approach.