An Approach to Perform Aspect level Sentiment Analysis on Customer Reviews using Sentiscore Algorithm and Priority Based Classification

Abstract — This paper analyses the customer reviews on restaurant domain using sentiment analysis and text mining techniques. The most integral part of our work is to assign Sentiment scores to the aspects with respect to the words used. We have devised Sentiscore algorithm to perform this function. The dataset we have at our disposal is a set of review documents obtained from an authenticated repository. We perform an aspect level sentiment extraction thereby, attempting to mine and understand the user's feedback data. The aspects that we have taken into account are food, cost, ambience and service. A priority-based algorithm forms the rule base for the classifier to predict the polarity of the reviews. To start with, we perform a clean up on the review data. Sentiscore algorithm is then utilized to generate the aspect document matrix. Polarity prediction is performed using Naïve Bayes and k-nn classifier and the results are evaluated. Finally, the experimental analysis shows that, k-nn performs better with increasing number of instances. The proposed methodology will equip the restaurant owner to identify the areas that require improvement