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. Keywords: Reviews; Opinion mining; Necessities; Preferences; Modals; Random Forest
Highlights
In this work filtering and categorising of hotel reviews based on modals are carried out and this is further used for opinion mining
Reviews which have Modals in it are considered. Further these reviews are categorized into two different classes based on different modals
Reviews with each type of required modal is considered in particular files and later the model is trained by giving these review files with different modals as input
Summary
In this work filtering and categorising of hotel reviews based on modals are carried out and this is further used for opinion mining. The proposed method for feature-based opinion mining for Hotel reviews has several steps. The various steps involved in this opinion mining are input reviews collection, input data pre-processing, feature extraction along with the opinion indicating modal extraction, and result summarization which are described below. The input data contains reviews about the features of the Hotel i.e., about room, food, WIFI Service etc. The first step in pre-processing is to split the extracted reviews into sentences and to a list of words. Tokenized data are tagged using Parts of Speech tagger (POS)
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