Estimating Filipino ISPs Customer Satisfaction Using Sentiment Analysis
Frederick F. Patacsil, Alvin R. Malicdem, Proceso L. Fernandez · Computer Science and Information Technology · 2015
Sentiment Analysis (SA) combines Natural Language Processing (NLP) techniques and text analytics to extract useful information from textual data. This study uses SA to estimate the Filipino internet customers' satisfaction related to the quality of the service provided by the Internet Service Providers (ISPs). Data were collected from Blog comments shared with online social media. Automatic word seed selection was applied using the word pair set {“Good” and “Slow”} as initial seed for the word dictionary. The Naïve Bayes method was used as a classifying tool to identify the dominant words used to express customers' sentiments and to determine the sentiment polarity of their opinions. The proposed automatic classifier successfully identifies positive and negative polarity of the blog sentences with a 91.50% accuracy in the training set. However, the results of the actual evaluation of the manually labelled test set show a drop in accuracy rate of 60.27%. Some of the reasons for this drop in accuracy are investigated in this paper.