• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2021, Volume: 14, Issue: 40, Pages: 3064-3073

Original Article

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

Received Date:05 June 2021, Accepted Date:20 October 2021, Published Date:12 February 2021


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


  1. Mukherjee S, Bhattacharyya P. Feature Specific Sentiment Analysis for Product Reviews. Computational Linguistics and Intelligent Text Processing. 2012;p. 475–487. Available from: https://doi.org/10.1007/978-3-642-28604-9_39
  2. Fang X, Zhan J. Sentiment analysis using product review data. Journal of Big Data. 2015;2(1). Available from: https://dx.doi.org/10.1186/s40537-015-0015-2
  3. Hu YH, Chen YL, Chou HL. Opinion mining from online hotel reviews – A text summarization approach. Information Processing & Management. 2017;53(2):436–449. Available from: https://dx.doi.org/10.1016/j.ipm.2016.12.002
  4. Păvăloaia VD, Teodor EM, Fotache D, et al. Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences. Sustainability. 2019;11(16):4459. Available from: https://dx.doi.org/10.3390/su11164459
  5. Slanzi G, Pizarro G, Velásquez JD. Biometric information fusion for web user navigation and preferences analysis: An overview. Information Fusion. 2017;38:12–21. Available from: https://dx.doi.org/10.1016/j.inffus.2017.02.006
  6. Mohammed B, Mouhoub M, Alanazi E, Sadaoui S. Data Mining Techniques and Preference Learning in Recommender Systems. Computer and Information Science. 2013;6(4). Available from: https://dx.doi.org/10.5539/cis.v6n4p88
  7. Holland S, Ester M, Kießling W. Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications. In: Knowledge Discovery in Databases: PKDD 2003. (pp. 204-216) Springer Berlin Heidelberg. 2003.
  8. Gao Y, Meyer CM, Gurevych I. Preference-based interactive multi-document summarisation. Information Retrieval Journal. 2020;23(6):555–585. Available from: https://dx.doi.org/10.1007/s10791-019-09367-8
  9. Marrese-Taylor E, Velásquez JD, Bravo-Marquez F, Matsuo Y. Identifying Customer Preferences about Tourism Products Using an Aspect-based Opinion Mining Approach. Procedia Computer Science. 2013;22:182–191. Available from: https://dx.doi.org/10.1016/j.procs.2013.09.094
  10. Bogerd Nvd, Dijkstra SC, Seidell JC, Maas J. Greenery in the university environment: Students’ preferences and perceived restoration likelihood. PLOS ONE. 2018;13(2):e0192429. Available from: https://dx.doi.org/10.1371/journal.pone.0192429


© 2021 Chigateri & Bhandarkar. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.