SENTIMENT ANALYSIS OF TEXT REVIEWS USING TONE DICTIONARIES AND FUZZY SET CARDINALITY
Abstract
Sentiment or opinion analysis aims to determine the polarity of people's opinions in relation
to any product, service, event or any person. One of the most common methods used in sentiment
analysis of text content is natural language processing. Sentiment analysis of natural language
text can be assessed using numerous methodologies such as machine learning algorithms and
statistical tools, while the application of fuzzy logic is not common. The use of fuzzy logic was
chosen for the following reasons. First, fuzzy logic handles linguistic uncertainty well. This way of
defining the problem leads to a reduction in bias, both positively and negatively. Secondly, learn ing approaches based on fuzzy rules are fundamentally different from those learning approaches
that are widely used in sentiment classification, such as support vector machines, naive Bayes,
etc., as they relate to generative learning, i.e. i.e. the goal of learning is to assess the degree to
which an instance belongs to each individual class. The proposed model for sentiment analysis of
text reviews is based on the use of tone lexicons using fuzzy logic and consists of four main stages.
The steps include tokenization, word bag model formulation, sentiment fuzzy score formulation,
and polarity assignment. In the proposed model, the power of the fuzzy set is used as a measure of
the evaluation of the indicators of the polarity of words. Word polarity values are obtained by
applying two sentiment lexicons: SentiWordNet and AFINN. Two versions of the model were created
depending on the type of vocabulary used: based on SentiWordNet and AFINN. Comparison
of the presented approach based on fuzzy logic with other dictionary-based methods demonstrates
the superiority of the developed models based on the application of fuzzy logic.
References
Conference on Computational Intelligence and Computing Research (ICCIC), 2015,
pp. 1-6.
2. Li X., Xie H., Chen L., Wang J., and Deng X. News impact on stock price return via sentiment
analysis, Knowledge-Base Systems, 2014, Vol. 69, pp. 14- 23.
3. Bose R., Dey R.K., Roy S., Sarddar D. Analyzing Political Sentiment Using Twitter Data, Information
and Communication Technology for Intelligent Systems. Springer, Singapore, 2019,
pp. 427-436.
4. Dundar B., Ozdemir S., Akay D. Opinion mining and fuzzy quantification in hotel reviews,
2016 International Symposium on Networks, Computers and Communications (ISNCC), 2016,
pp. 1-4.
5. Park E., Kang J., Choi D.,Han J. Understanding customers' hotel revisiting behaviour: a sentiment
analysis of online feedback reviews, Current Issues in Tourism, 2020, Vol. 23, No. 5,
pp. 605-611.
6. Mostafa L. Machine Learning-Based Sentiment Analysis for Analyzing the Travelers Reviews
on Egyptian Hotels, Joint European-US Workshop on Applications of Invariance in Computer
Vision, Springer, Cham, 2020, pp. 405-413.
7. Vashishtha S., Susan S. Fuzzy logic based dynamic plotting of mood swings from tweets, International
Conference on Innovations in Bio-Inspired Computing and Applications. Springer,
Cham, 2018, pp. 129-139.
8. Shivaprasad T.K., Shetty J. Sentiment analysis of product reviews: a review, International
Conference on Inventive Communication and Computational Technologies (ICICCT), 2017,
pp. 298-301.
9. Liu Y., Bi J.W., Fan Z. P. Ranking products through online reviews: A method based on sentiment
analysis technique and intuitionistic fuzzy set theory, Information Fusion, 2017, Vol. 36.
pp. 149-161.
10. Indhuja K., Reghu RPC. Fuzzy logic based sentiment analysis of product review documents,
2014 First International Conference on Computational Systems and Communications
(ICCSC), 2014, pp. 18-22.
11. Zadeh L.A. Calculus of Fuzzy Restrictions. Fuzzy Sets and their Applications to cognitive and
decision processes, 1975, pp. 1-40.
12. Singh W. Sentiment analysis of online mobile reviews, 2017 International Conference on Inventive
Communication and Computational Technologies (ICICCT), 2017, pp. 20-25.
13. Yadav P., Pandya D. SentiReview: Sentiment analysis based on text and emoticons, International
Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 2017,
pp. 467-472.
14. Songpan W. The analysis and prediction of customer review rating using opinion mining, 15th
International Conference on Software Engineering Research, Management and Applications
(SERA), 2017, pp. 71-77.
15. Dudani A., Srividya V., Sneha B., Tripathy B. K. Sentiment Analysis on Kerala Floods, International
Conference on Innovative Computing and Communications, Springer, Singapore,
2020, pp. 107-124.
16. Singh P., Sawhney R.S., Kahlon K.S. Sentiment analysis of demonetization of 500 & 1000
rupee banknotes by Indian government. ICT Express, 2018, Vol. 4, No. 3, pp. 124-129.
17. Kumar A., Singh J.P. Demonetization in India: Good or Bad in Context of Social Media.
– 2019.
18. Jusoh S., Alfaware H. M. Applying fuzzy sets for opinion mining, International Conference on
Computer Applications Technology (ICCAT), 2013, pp. 1-5.
19. Alharbi J.R., Alhalabi W.S. Hybrid Approach for Sentiment Analysis of Twitter Posts Using a
Dictionary-based Approach and Fuzzy Logic Methods: Study Case on Cloud Service Providers,
International Journal on Semantic Web and Information Systems (IJSWIS), 2020, Vol. 16,
No. 1, pp. 116-145.
20. Phan H.T., Nguyen N.T., Cuong T.V., Hwang D. A Method for Detecting and Analyzing the
Sentiment of Tweets Containing Fuzzy Sentiment Phrases, 2019 IEEE International Symposium
on INnovations in Intelligent SysTems and Applications (INISTA), 2019, pp. 1-6.
21. Phan H.T., Tran V.C., Nguyen N.T., Hwang D. Improving the performance of sentiment analysis
of tweets containing fuzzy sentiment using the feature ensemble model, IEEE Access, 2020,
Vol. 8, pp. 14630-14641.
22. Pang B., Lee L. A sentimental education: Sentiment analysis using subjectivity summarization
based on minimum cuts, Proceedings of the 42nd annual meeting on Association for Computational
Linguistics, 2004, pp. 271.
23. Cavalcanti D. C., Ricardo B.C .Prudêncio, Shreyasee S. Pradhan, Jatin Y. Shah, Ricardo S.
Pietrobon. Good to be bad? Distinguishing between positive and negative citations in scientific
impact, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence,
2011, pp. 156-162.
24. Hutto C. J., Gilbert E. Vader: A parsimonious rule-based model for sentiment analysis of social
media text, Eighth international AAAI conference on weblogs and social media. – 2014.
