Access Type

Open Access Thesis

Date of Award

January 2019

Degree Type


Degree Name



Computer Science

First Advisor

Alexander Kotov


Online consumer reviews provide a wealth of information about products and services that, if properly identified and extracted, could be of immense value to businesses. While classification of reviews according to sentiment polarity has been extensively studied in previous work, many more focused types of review analysis remain open problems. In this work, we introduce a novel text classification problem of separating post-purchase from pre-purchase consumer review fragments that can facilitate identification of immediate actionable insights based on the feedback from the customers, who actually purchased and own a product. To address this problem, we propose the features, which are based on the dictionaries and part-of-speech (POS) tags. Experimental results on the publicly available gold standard indicate that the proposed features allow to achieve nearly 75% accuracy for this problem and improve the performance of classifiers relative to using only lexical features.