Necessity and Preference Mining from Text Reviews: A Non-bipolar assessment of Text Reviews

Abstract

Objective: To perform opinion mining on text reviews related to hotel. Methods: In this work, the opinion is mined by identifying and extracting necessities and preferences along with the associated two features or aspects expressed in text reviews by customers. The hotel dataset (From Kaggle website, hotels in United States, has 35912 samples) is considered for training and testing. Modals 'Has' and 'Would' are used to identify and extract reviews which are expressing the necessities and preferences of customers from the dataset of hotel reviews. Random Forest machine learning algorithm method is used for classifying the reviews belonging to necessity and preference categories. Findings: From the related works carried out so far, it is indeed transparent that so far, the text reviews are analysed for general sentiments like good, bad etc., polarities like positive, negative or neutral and emotions like joy, fear etc., The analysis for necessities and preferences in the text is yet to be addressed. The current research focuses on narrowing the semantic gap in opinion mining from Generalized analysis of reviews like positive, negative, good, bad to Specialized analysis of reviews like mining necessities and preferences of customers which may give higher level of understanding of customer needs by service providers. In this work, the reviews are classified into two classes viz, necessities and preferences are identified and classified using Random Forest machine learning algorithm. It gave the accuracy of 91% in classifying the reviews as necessity and 99.78% in classifying the reviews as preferences by using the formula given in the system implementation section. Novelty: Classification of reviews into Necessity and preference classes.