1. Introduction

2. Reviews’ Usefulness and Reviewers’ Bias in Sentiment Analysis

3. Methodology

• Examine how informative the users from different nationalities are, i.e., examine cultural differences in terms of reviews’ articulacy.
• Propose a fuzzy synthetic evaluation approach that allows users to identify the most useful reviews to read. Although this study considers the sentiment and articulacy determinants of reviews’ usefulness, the proposed approach allows users to specify their personalised perspective of usefulness by incorporating their individual biases. Figure 1 illustrates the steps of the proposed methodology.

Figure 1. The steps of the proposed methodology.

Figure 1. The steps of the proposed methodology.

4. Methods

4.2. Fuzzy Synthetic Evaluation

The Fuzzy Synthetic Evaluation (FSE) has been widely used to assess muti-criteria problems [63,64,65,66]. The FSE conceptualizes a decision making problem at three levels: the indicators, the criteria, and the alternatives [63,64,65]. It associates the three levels drawing on fuzzy relations. The steps of FSE follow:

i)

Assume that $\mathrm{C}=\left\{C_i,\mathrm{i}=1,\mathrm{...},\mathrm{n}\right\}$ is the set of criteria, and $C_i$ indicates criterion (i). This study assumes the criterion “usefulness” thus, $n=1$.

ii)

Assume $\mathrm{I}=\left\{I_j,\mathrm{j}=1,\mathrm{...},\mathrm{m}\right\}$ is the set of indicators, where $I_j$ indicates indicator (j). It consists of the “title-sentiment (t_s)”, “review-sentiment (r_s)”, “title-articulacy (t_a)” and the “review-articulacy (r_a)” indicators thus, $m=4$.

iii)

$\mathrm{AG}=\left\{{\mathrm{AG}}_p,\mathrm{p}=1,\mathrm{...},\mathrm{s}\right\}$ is the set of assessment grades for criteria, indicators, and alternatives, with ${\mathrm{AG}}_p$ indicating assessment grades.

More specifically,

the set of assessment grades ${\mathrm{AG}}_p^{Ci}$ used for the criterion “usefulness” is, ${\mathrm{AG}}^{Ci}=\left\{{\mathrm{AG}}_p^{Ci},\mathrm{p}=1,\mathrm{...}{,\mathrm{k}}_{\mathrm{Ci}}\right\}=\left\{\mathrm{Low},\mathrm{Medium},\mathrm{High}\right\}$.

Respectively for the indicators,

${\mathrm{AG}}^{\mathrm{Ij}}=\left\{{\mathrm{AG}}_p^{\mathrm{Ij}},\mathrm{p}=1,\mathrm{...}{,\mathrm{k}}_{\mathrm{Ij}}\right\}$, for $I_1,I_2,I_3,I_4$, which in our case are $I_{ts},I_{rs},I_{ta},I_{ra}$

$${\mathrm{AG}}^{I_1}=\left\{{\mathrm{AG}}_p^{I_1},\mathrm{p}=1,\mathrm{...}{,\mathrm{k}}_{I_1}\right\}=\left\{{\mathrm{AG}}_p^{t_s},\mathrm{p}=1,\mathrm{...},3\right\}=\left\{\mathrm{Negative},\mathrm{Neutral},\mathrm{Positive}\right\}$$

$${\mathrm{AG}}^{I_2}=\left\{{\mathrm{AG}}_p^{I_2},\mathrm{p}=1,\mathrm{...}{,\mathrm{k}}_{I_2}\right\}=\left\{{\mathrm{AG}}_p^{r_s},\mathrm{p}=1,\mathrm{...},3\right\}=\left\{\mathrm{Negative},\mathrm{Neutral},\mathrm{Positive}\right\}$$

$${\mathrm{AG}}^{I_3}=\left\{{\mathrm{AG}}_p^{I_3},\mathrm{p}=1,\mathrm{...}{,\mathrm{k}}_{I_3}\right\}=\left\{{\mathrm{AG}}_p^{t_a},\mathrm{p}=1,\mathrm{...},3\right\}=\left\{\mathrm{Low},\mathrm{Medium},\mathrm{High}\right\}$$

$${\mathrm{AG}}^{I_4}=\left\{{\mathrm{AG}}_p^{I_4},\mathrm{p}=1,\mathrm{...}{,\mathrm{k}}_{I_4}\right\}=\left\{{\mathrm{AG}}_p^{r_a},\mathrm{p}=1,\mathrm{...},3\right\}=\left\{\mathrm{Low},\mathrm{Medium},\mathrm{High}\right\}$$

All the above consist of three grades, therefore $k_{C_1}=k_{I_1}=k_{I_2}=k_{I_3}=k_{I_4}=3$.

iv)

$\mathrm{A}=\left\{r_v,\mathrm{v}=1,\mathrm{...},\mathrm{z}\right\}$ is the set of the alternatives, where (z) is the number of reviews that are potentially considered by the users when seeking advice for a destination.

v)

Establish the membership function matrix $\mathrm{R}$ for each nationality $\mathrm{Nat}=\left\{{\mathrm{Nat}}_g,\mathrm{g}=1,\mathrm{...},\mathrm{d}\right\}$,

(in our case $d=5,\left\{\mathrm{British},\mathrm{US},\mathrm{Australian},\mathrm{Greek},\mathrm{Dutch}\right\}$):

(4)

where $r_{z,I_j}$ indicates the membership degrees to which $I_j$ satisfies assessment grade ${\mathrm{AG}}_{k_{\mathrm{Ij}}}^{\mathrm{Ij}}$, to the total of reviews (z).

For example, in order to calculate the membership degree to which indicator “title sentiment - $t_s$”, indicator, $I_1$, satisfies assessment grade, ${\mathrm{AG}}_1^{I_1}$, “Negative” (element (1,1) of the matrix above), we calculate the percentage of the total reviews that are related as “Negative”. Respectively, we calculate the percentage of the total reviews that are related as “Neutral” and “Positive”.

Let assume that the calculation returns that 17% of the British reviews are rated as “Negative”, 23% as “Neutral” and 60% as “Positive”; then the membership function of the “title-sentiment - $t_s$”, is given by (5):

$$\frac{0,17}{\mathrm{Negative}},\frac{0,23}{\mathrm{Neutral}},\frac{0,60}{\mathrm{Positive}}$$

(5)

Each of the three assessment grades is assigned to a rating factor $S_p=1,2,3$, e.g. Negative=1, Neutral=2, and Positive=3, and Low=1, Medium=2, and High=3, as used in other studies [63,64,66,67].

$$\frac{0,17}{\mathrm{Negative}},\frac{0,23}{\mathrm{Neutral}},\frac{0,60}{\mathrm{Positive}}\to \frac{0,17}{1}+\frac{0,23}{2}+\frac{0,60}{3}$$

vi)

Calculate the weights ${\mathrm{WI}}_j$ for each indicator $I_j$. This study adopts the ordered weight averaging aggregation (OWA), which is often used in fuzzy logic [63,67]. The weights are calculated using the equations (6) and (7):

$${}_{}{}^{{\mathrm{Nat}}_g}\mathrm{I}{\mathrm{mp}}_{I_j}={{\displaystyle \sum }}_p\left(S_p\times r_{z,I_j}\right)$$

(6)

$${}_{}{}^{{\mathrm{Nat}}_g}\mathrm{W}{\mathrm{I}}_{I_j}=\frac{\left({}_{}{}^{{\mathrm{Nat}}_g}\mathrm{I}{\mathrm{mp}}_{I_j}\right)}{{{\displaystyle \sum }}_{j=1}^m\left({}_{}{}^{{\mathrm{Nat}}_g}\mathrm{I}{\mathrm{mp}}_{I_j}\right)}$$

(7)

where

${}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{I}{\mathrm{mp}}_{{\mathrm{I}}_{\mathrm{j}}}$ is the aggregated importance vector for indicator $I_j$,

$S_p$ is the rating factor given to assessment grade ${\mathrm{AG}}_{k_{\mathrm{Ij}}}^{\mathrm{Ij}}$, and

m is the number of indicators under one criterion.

Therefore, the vector of weights for the (m) indicators is given by:

$${}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{W}\mathrm{I}=\left[\begin{array}{c}{}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{W}{\mathrm{I}}_{{\mathrm{I}}_1}\\ {}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{W}{\mathrm{I}}_{{\mathrm{I}}_2}\\ \cdots \\ {}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{W}{\mathrm{I}}_{{\mathrm{I}}_{\mathrm{m}}}\end{array}\right]=\left[\begin{array}{c}{}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{W}{\mathrm{I}}_{{\mathrm{t}}_{\mathrm{s}}}\\ {}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{W}{\mathrm{I}}_{{\mathrm{r}}_{\mathrm{s}}}\\ {}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{W}{\mathrm{I}}_{{\mathrm{t}}_{\mathrm{a}}}\\ {}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{W}{\mathrm{I}}_{{\mathrm{r}}_{\mathrm{a}}}\end{array}\right]$$

(8)

vii)

Calculate the weights ${\mathrm{WC}}_i$, for each criterion $C_i$. The weights are calculated using the equation (9).

$${}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{WC}_{\mathrm{i}}=\frac{{({\displaystyle \sum _{j=1}^m{}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{W}{\mathrm{I}}_{{\mathrm{I}}_{\mathrm{j}}}})}_i}{{({\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^m{}^{{\mathrm{Nat}}_{\mathrm{g}}}\mathrm{W}{\mathrm{I}}_{{\mathrm{I}}_{\mathrm{j}}}}})}_i},$$

(9)

for all indicators under criterion $C_i$.

This study assumes one criterion, i.e. the “usefulness” of reviews.

viii)

Establish the membership function matrix APF of the alternatives’ performance for each nationality

(10)

$$\begin{array}{r}{\mathrm{A}}_{{\mathrm{Nat}}_1}{=\text{{}\mathrm{r}}_{{\mathrm{v}}_{{\mathrm{Nat}}_1}}{,\mathrm{v}}_{{\mathrm{Nat}}_1}=1,\mathrm{...}{,\mathrm{z}}_{{\mathrm{Nat}}_1}{\text{}},\mathrm{where}\mathrm{z}}_{{\mathrm{Nat}}_1}\mathrm{is}\mathrm{the}\mathrm{total}\mathrm{number}\mathrm{of}\mathrm{British}\mathrm{Reviews}\\ {\mathrm{A}}_{{\mathrm{Nat}}_2}{=\text{{}\mathrm{r}}_{{\mathrm{v}}_{{\mathrm{Nat}}_2}}{,\mathrm{v}}_{{\mathrm{Nat}}_2}=1,\mathrm{...}{,\mathrm{z}}_{{\mathrm{Nat}}_2}\text{}},\\ \dots \\ {\mathrm{A}}_{{\mathrm{Nat}}_5}{=\text{{}\mathrm{r}}_{{\mathrm{v}}_{{\mathrm{Nat}}_5}}{,\mathrm{v}}_{{\mathrm{Nat}}_5}=1,\mathrm{...}{,\mathrm{z}}_{{\mathrm{Nat}}_5}\text{}},\end{array}$$

$$\mathrm{A}={\displaystyle \sum }_{h=1}^d{A}_{{\mathrm{Nat}}_h}$$

$$\mathrm{z}={\displaystyle \sum }_{h=1}^d{z}_{{\mathrm{Nat}}_h}$$

$a_{z,p}$ indicates the membership degrees to which alternative $z$ satisfies assessment grade ${\mathrm{AG}}_{k_{\mathrm{Ij}}}^{\mathrm{Ij}}$. This study considers the set of reviews that a user may consider as the set of alternatives.

ix)

Aggregate performance evaluation for alternative $z$ using fuzzy composition [61,63,65]as shown in equation (11):

$${\omega }_z=W\otimes AP{F}_z$$

(11)

The three scalars of ${\omega }_p^z$, represent the membership degrees of each assessment grade $p$, for the alternative$z$, thus

$${\omega }_z\to \frac{{\omega }_1^z}{\mathrm{Negative}}+\frac{{\omega }_2^z}{\mathrm{Neutral}}+\frac{{\omega }_3^z}{\mathrm{Positive}}$$

A crisp value for ${\omega }_z$ can be obtained after defuzzification. This study adopts the equations (15) and (16) used in [63] in order to calculate the score for alternative$z$:

$${\omega }_z=5{\omega }_{\mathrm{Negative}}^z+50{\omega }_{\mathrm{Neutral}}^z+100{\omega }_{\mathrm{Positive}}^z$$

(12)

$$\mathrm{A}\mathrm{final}\mathrm{usefulness}\mathrm{score}\mathrm{is}\mathrm{given}\mathrm{by}{\Delta }_z=100-{\omega }_z$$

(13)

5. Results

5.1. Reviews’ Sentiments membership functions

Results indicate that the British exhibit more consistent behaviour with their sentiments expressed in reviews’ titles and bodies in all three sentiments categories than the other nationalities in the sample.

Table 1. Title and Review sentiment percentages for each nationality in the sample.

Table 1. Title and Review sentiment percentages for each nationality in the sample.

	Sentiment
	Title	Review	Title	Review	Title	Review
	Negative		Neutral		Positive
British	4,08	4,68	6,83	6,65	89,08	88,67
USA	4,73	32,21	5,89	7,10	89,37	60,67
Australian	2,60	28,74	6,46	6,40	90,94	64,86
Greek	3,55	6,26	6,68	24,13	89,77	69,61
Dutch	3,86	30,68	6,69	4,56	89,45	64,76

The sentiments British expressed in either their review’s titles or the reviews’ documents are almost identical. All nationalities in the sample are unanimous in expressing more positive than negative sentiments to a large extent, which is a good sign for the quality of the services reviewers received during their visits.

Differences range from a 20,16% more positive evaluations in titles than in full texts in the Greek sample, to a 28,7% in the USA sample. Similarly, more negative evaluations are found in full documents than in titles. Sentiment frequencies vary from a minimum of 2,71% more negatives in the full documents in the Greek sample, to the maximum of 27,48% in the USA sample. This implies that when the Greeks express negative sentiments in their titles, they do so more concisely and more accurately than the other three nationalities. For the Australians, and Dutch samples show larger percentage differences which are 26,14% and 26,82% respectively. With respect to neutral sentiments, percentage differences between titles and full reviews are rather small ranging from almost 0 to 2,13%, with the exception of the Greek sample (17,45%).

To accommodate the differences in users’ behaviour, this study proposes to represent sentiments from different nationalities as triangular fuzzy sets $\tilde{A}\left(l,m_i,u_i\right)$. According to [64,66,67], the membership function of each sentiment fuzzy set is formed as follows: In the sample of the total 42.678 British reviews, 1.742 expressed negative sentiment in their title, which returns a 4% negative reviews. Similarly, neutral titles account for a 6%, and positive ones for an 89%. Thus, for the British title sentiments the membership function is: $\frac{0,04}{Negative}+\frac{0,06}{Neutral}+\frac{0,89}{Positive}\to \frac{0,04}{1}+\frac{0,06}{2}+\frac{0,89}{3}$. The rating given to each assessment grade (i.e. Negative=1, Neutral=2, Positive=3), is adopted by [63,64,66,67]. The membership functions for both titles and reviews sentiments for each nationality, are shown in Table 2.

Figure 2 clearly depicts the differences between the British and the USA reviews’ sentiment fuzzy sets, which imply the behavioural differences between the two nationalities.

The sentiments’ fuzzy sets with their membership functions show the level of “positiveness” in reviews for each nationality. The diagrams show that British and Greek reviewers are inclined towards more positive comments than the other nationalities in the sample. The USA, Australians and Dutch users’ sentiments are closer for both titles and reviews.

6. Discussion

7. Limitations of the study and Future Research

8. Conclusions

References

1. B. Liu, Sentiment Analysis Essentials Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge, 2015.
2. L. Yu, L. Wang, D. Liu, and Y. Liu, ‘Research on Intelligence Computing Models of Fine-Grained Opinion Mining in Online Reviews’, IEEE Access, vol. 7, pp. 116900–116910, 2019. [CrossRef]
3. V. Oktaviani, B. Warsito, H. Yasin, R. Santoso, and Suparti, ‘Sentiment Analysis of e-Commerce Application in Traveloka Data Review on Google Play Site Using Naïve Bayes Classifier and Association Method’, in Journal of Physics: Conference Series, IOP Publishing Ltd, Jul. 2020. [CrossRef]
4. C. Wang, X. Zhu, and L. Yan, ‘Sentiment Analysis for E-Commerce Reviews Based on Deep Learning Hybrid Model’, in 5th International Conference on Signal Processing and Machine Learning (SPML), Association for Computing Machinery, Aug. 2022, pp. 38–46. [CrossRef]
5. P. Savci and B. Das, ‘Prediction of the Customers’ Interests Using Sentiment Analysis in e-Commerce Data for Comparison of Arabic, English, and Turkish Languages’, Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 3, pp. 227–237, Mar. 2023. [CrossRef]
6. M. J. Hossain, D. Das Joy, S. Das, and R. Mustafa, ‘Sentiment Analysis on Reviews of e-commerce Sites Using Machine Learning Algorithms’, in International Conference on Innovations in Science, Engineering and Technology (ICISET) 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 522–527. [CrossRef]
7. P. Rajesh and G. Suseendran, ‘Prediction of N-Gram Language Models Using Sentiment Analysis on E-Learning Reviews ’, International Conference on Intelligent Engineering and Management (ICIEM), pp. 510–514, 2020. [CrossRef]
8. M. A. dos Santos Alencar, J. F. de Magalhães Netto, and F. de Morais, ‘A Sentiment Analysis Framework for Virtual Learning Environment’, Applied Artificial Intelligence, vol. 35, no. 7, pp. 520–536, 2021. [CrossRef]
9. X. Yan, F. Jian, and B. Sun, ‘SAKG-BERT: Enabling Language Representation with Knowledge Graphs for Chinese Sentiment Analysis’, IEEE Access, vol. 9, pp. 101695–101701, 2021. [CrossRef]
10. S. F. Sayeedunnisa and M. Hijab, ‘Impact of e-Learning in Education Sector: A Sentiment Analysis View’, in IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Institute of Electrical and Electronics Engineers Inc., 2022, pp. 1–5. [CrossRef]
11. L. K. Singh and R. Renuga Devi, ‘Analysis of Student Sentiment Level Using Perceptual Neural Boltzmann Machine Learning Approach for E-learning Applications’, in 5th International Conference on Inventive Computation Technologies, ICICT 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 1270–1276. [CrossRef]
12. Z. Khanam, ‘Sentiment Analysis of User Reviews in an Online Learning Environment: Analyzing the Methods and Future Prospects’, European Journal of Education and Pedagogy, vol. 4, no. 2, pp. 209–217, Apr. 2023. [CrossRef]
13. Krouska, Troussas, and Virvou, ‘Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-Trained Word Embeddingd’, in Machine Learning Paradigms. Learning and Analytics in Intelligent Systems, Springer Cham., vol. 18, Tsihrintzis G and Jain L, Eds., 2020. [CrossRef]
14. L. Li, Y. Wu, Y. Zhang, and T. Zhao, ‘Time+User Dual Attention Based Sentiment Prediction for Multiple Social Network Texts with Time Series’, IEEE Access, vol. 7, pp. 17644–17653, 2019. [CrossRef]
15. T. Wang, K. Lu, K. P. Chow, and Q. Zhu, ‘COVID-19 Sensing: Negative Sentiment Analysis on Social Media in China via BERT Model’, in IEEE Access, Institute of Electrical and Electronics Engineers Inc., 2020, pp. 138162–138169. [CrossRef]
16. F. Alattar and K. Shaalan, ‘Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media’, IEEE Access, vol. 9, pp. 61756–61767, 2021. [CrossRef]
17. H. Silva, E. Andrade, D. Araújo, and Dantas J., ‘Sentiment Analysis of Tweets Related to SUS Before and During COVID-19 Pandemic’, Proceedings of the IEEE Latin America Transactions, vol. 20, no. 1, pp. 6–13, 2022. [CrossRef]
18. M. Rodríguez-Ibánez, A. Casánez-Ventura, F. Castejón-Mateos, and P. M. Cuenca-Jiménez, ‘A review on Sentiment Analysis from Social Media Platforms’, Expert Systems with Applications, vol. 223. Elsevier Ltd, Aug. 01, 2023. [CrossRef]
19. A. Krouska, C. Troussas, and M. Virvou, ‘Comparative Evaluation of Algorithms for Sentiment Analysis over Social Networking Services’, Journal of Universal Computer Science (JUCS), vol. 23, no. 8, pp. 755–768, 2017, [Online]. Available: http://www.internetlivestats.com/.
20. J. Usher, L. Morales, and P. Dondio, ‘BREXIT: A Granger Causality of Twitter Political Polarisation on the FTSE 100 Index and the Pound’, in Proceedings - IEEE 2nd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2019, Institute of Electrical and Electronics Engineers Inc., Jun. 2019, pp. 51–54. [CrossRef]
21. N. Shaghaghi, A. M. Calle, J. Manuel Zuluaga Fernandez, M. Hussain, Y. Kamdar, and S. Ghosh, ‘Twitter Sentiment Analysis and Political Approval Ratings for Situational Awareness’, Proceedings of the IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), pp. 59–65, 2021. [CrossRef]
22. A. Schmale and V. Mittendorf, ‘Detecting Negative Campaigning on Twitter Against The Greens’, Proceedings of the IEEE Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8, 2022. [CrossRef]
23. S. Orellana and H. Bisgin, ‘Using Natural Language Processing to Analyze Political Party Manifestos from New Zealand †’, Information (Switzerland), vol. 14, no. 3, p. 152, Mar. 2023. [CrossRef]
24. A. Ligthart, C. Catal, and B. Tekinerdogan, ‘Systematic Reviews in Sentiment Analysis: a Tertiary Study’, Artif Intell Rev, vol. 54, no. 7, pp. 4997–5053, Oct. 2021. [CrossRef]
25. M. Thelwall, D. Wilkinson, and S. Uppal, ‘Data Mining Emotion in Social Network Communication: Gender Differences in MySpace’, Journal of the American Society for Information Science and Technology, vol. 61, no. 1, pp. 190–199, Jan. 2010. [CrossRef]
26. S. Volkova and B. Yoram, ‘On Predicting Sociodemographic Traits and Emotions from Communications in Social Networks and their Implications to Online Self-Disclosure’, Cyberpsychology, Behavior, and Social Networking. 2015, 18, 726–736. [CrossRef]
27. M. B. Babac and V. Podobnik, ‘A Sentiment Analysis of Who Participates, How and Why, at Social Media Sport Websites: How Differently Men and Women Write About Football’, Online Information Review, vol. 40, no. 6, pp. 814–833, 2016. [CrossRef]
28. F. Rangel and P. Rosso, ‘On the Impact of Emotions on Author Profiling’, Inf Process Manag, vol. 52, no. 1, pp. 73–92, Jan. 2016. [CrossRef]
29. M. Thelwall, ‘Gender Bias in Sentiment Analysis’, Online Information Review, vol. 42, no. 1, pp. 45–57, 2018. [CrossRef]
30. P. Rajshakhar, A. Bosu, and S. Z. Kazi, ‘Expressions of Sentiments During Code Reviews: Male vs. Female’, IEEE 26th international conference on software analysis, evolution and reengineering (SANER), pp. 26–37, 2019. [CrossRef]
31. T. Sun et al., ‘Mitigating Gender Bias in Natural Language Processing: Literature Review’, in 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2019, pp. 1630–1640. Accessed: Feb. 24, 2024. [Online]. Available:. [CrossRef]
32. F. Villarroel Ordenes and R. Silipo, ‘Machine Learning for Marketing on the KNIME Hub: The Development of a Live Repository for Marketing Applications’, J Bus Res, vol. 137, pp. 393–410, Dec. 2021. [CrossRef]
33. M. López, A. Valdivia, E. Martínez-Cámara, M. V. Luzón, and F. Herrera, ‘E2SAM: Evolutionary Ensemble of Sentiment Analysis Methods for Domain Adaptation’, Inf Sci (N Y), vol. 480, pp. 273–286, Apr. 2019. [CrossRef]
34. S. R. Davis, C. J. Worsnop, and E. M. Hand, ‘Gender Bias Recognition in Political News Articles’, Machine Learning with Applications, vol. 8, no. 100304, Jun. 2022. [CrossRef]
35. J. M. Kim, M. Jun, and C. K. Kim, ‘The Effects of Culture on Consumers’ Consumption and Generation of Online Reviews’, Journal of Interactive Marketing, vol. 43, pp. 134–150, Aug. 2018. [CrossRef]
36. S. W. Litvin, ‘Hofstede, Cultural Differences, and TripAdvisor hotel Reviews’, International Journal of Tourism Research, vol. 21, no. 5, pp. 712–717, Sep. 2019. [CrossRef]
37. E. W. T. Ngai, V. C. S. Heung, Y. H. Wong, and F. K. Y. Chan, ‘Consumer Complaint Behaviour of Asians and non-Asians About Hotel Services: An Empirical Analysis’, Eur J Mark, vol. 41, no. 11–12, pp. 1375–1391, 2007. [CrossRef]
38. H. S. Choi and S. Leon, ‘An Empirical Investigation of Online Review Helpfulness: A Big Data Perspective’, Decis Support Syst, vol. 139, no. 113403, Dec. 2020. [CrossRef]
39. X. Zhang, X. Zhang, S. Liang, Y. Yang, and R. Law, ‘Infusing New Insights: How Do Review Novelty and Inconsistency Shape the Usefulness of Online Travel Reviews?’, Tour Manag, vol. 96, no. 104703, Jun. 2023. [CrossRef]
40. D. H. Park and J. Lee, ‘eWOM Overload and its Effect on Consumer Behavioral Intention Depending on Consumer Involvement’, Electron Commer Res Appl, vol. 7, no. 4, pp. 386–398, Dec. 2008. [CrossRef]
41. M. Siering, J. Muntermann, and B. Rajagopalan, ‘Explaining and Predicting Online Review Helpfulness: The Role of Content and Reviewer-Related signals’, Decis Support Syst, vol. 108, pp. 1–12, Apr. 2018. [CrossRef]
42. H. Hong, D. Xu, G. A. Wang, and W. Fan, ‘Understanding the Determinants of Online Review Helpfulness: A Meta-Analytic Investigation’, Decis Support Syst, vol. 102, pp. 1–11, Oct. 2017. [CrossRef]
43. S. Lee and J. Y. Choeh, ‘The Interactive Impact of Online Word-Of-Mouth and Review Helpfulness on Box Office Revenue’, Management Decision, vol. 56, no. 4, pp. 849–866, Mar. 2018. [CrossRef]
44. Q. Cao, W. Duan, and Q. Gan, ‘Exploring Determinants of Voting for the “Helpfulness” of Online User Reviews: A Text Mining Approach’, Decis Support Syst, vol. 50, no. 2, pp. 511–521, Jan. 2011. [CrossRef]
45. H. Baek, J. Ahn, and Y. Choi, ‘Helpfulness of Online Consumer Reviews: Readers’ Objectives and Review Cues’, International Journal of Electronic Commerce, vol. 17, no. 2. pp. 99–126, Jan. 01, 2012. [CrossRef]
46. P. Racherla and W. Friske, ‘Perceived “Usefulness” of Online Consumer Reviews: An Exploratory Investigation Across Three Services Categories’, Electron Commer Res Appl, vol. 11, no. 6, pp. 548–559, 2012. [CrossRef]
47. Z. Liu and S. Park, ‘What Makes a Useful Online Review? Implication for Travel Product Websites’, Tour Manag, vol. 47, pp. 140–151, Apr. 2015. [CrossRef]
48. Y. Zhang and Z. Lin, ‘Predicting the Helpfulness of Online Product Reviews: A Multilingual Approach’, Electron Commer Res Appl, vol. 27, pp. 1–10, Jan. 2018. [CrossRef]
49. S. Chatterjee, ‘Drivers of Helpfulness of Online Hotel Reviews: A Sentiment and Emotion Mining Approach’, Int J Hosp Manag, vol. 85, no. 102356, Feb. 2020. [CrossRef]
50. E. Bigne, C. Ruiz, A. Cuenca, C. Perez, and A. Garcia, ‘What Drives the Helpfulness of Online Reviews? A Deep Learning Study of Sentiment Analysis, Pictorial Content and Reviewer Expertise for Mature Destinations’, Journal of Destination Marketing and Management, vol. 20, p. 100570, Jun. 2021. [CrossRef]
51. L. Zhu, G. Yin, and W. He, ‘Is this Opinion Leader’s Review Useful? Peripheral Cues for Online Review Helpfulness’, Journal of Electronic Commerce Research, vol. 15, no. 4, p. 267, 2014.
52. S. M. Mudambi and D. Schuff, ‘What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com’, MIS Quarterly, vol. 34, no. 1, pp. 185–200, 2010, [Online]. Available: http://ssrn.com/abstract=2175066.
53. A. Y. K. Chua and S. Banerjee, ‘Understanding Review Helpfulness as a Function of Reviewer Reputation, Review Rating, and Review Depth’, Journal of the Association for Information Science and Technology, vol. 66, no. 2. John Wiley and Sons Inc., pp. 354–362, Feb. 01, 2015. [CrossRef]
54. B. Onikoyi, N. Nnamoko, and I. Korkontzelos, ‘Gender Prediction with descriptive Textual Data Using a Machine Learning Approach’, Natural Language Processing Journal, vol. 4, no. 100018, Sep. 2023. [CrossRef]
55. P. Rita, R. Ramos, M. T. Borges-Tiago, and D. Rodrigues, ‘Impact of the Rating System on Sentiment and Tone of Voice: A Booking.com and TripAdvisor Comparison Study’, Int J Hosp Manag, vol. 104, no. 103245, Jul. 2022. [CrossRef]
56. S. Gitto and P. Mancuso, ‘Improving Airport Services Using Sentiment Analysis of the Websites’, Tour Manag Perspect, vol. 22, pp. 132–136, Apr. 2017. [CrossRef]
57. G. Wojarnik, ‘Sentiment Analysis as a Factor Included in the Forecasts of Price Changes in the Stock Exchange’, in Procedia Computer Science, Elsevier B.V., 2021, pp. 3176–3183. [CrossRef]
58. M. Jiang, T. Y. Chen, and S. Wang, ‘On the Effectiveness of Testing Sentiment Analysis Systems with Metamorphic Testing’, Inf Softw Technol, vol. 150, no. 106966, Oct. 2022. [CrossRef]
59. N. Dhakate and R. Joshi, ‘Classification of Reviews of e-healthcare Services to Improve Patient Satisfaction: Insights from an Emerging Economy’, J Bus Res, vol. 164, pp. 114–115, Sep. 2023. [CrossRef]
60. H. Y. Lin, P. Y. Hsu, and G. J. Sheen, ‘A Fuzzy-Based Decision-Making Procedure for Data Warehouse System Selection’, Expert Syst Appl, vol. 32, no. 3, pp. 939–953, Apr. 2007. [CrossRef]
61. T. J. Ross, Fuzzy Logic with Engineering Applications, 3rd ed. John Wiley & Sons, 2010.
62. P. Luukka, ‘Similarity Classifier Using Similarities Based on Modified Probabilistic Equivalence Relations’, Knowl Based Syst, vol. 22, no. 1, pp. 57–62, Jan. 2009. [CrossRef]
63. G. Hu, H. Liu, C. Chen, P. He, J. Li, and H. Hou, ‘Selection of Green Remediation Alternatives for Chemical Industrial Sites: An Integrated Life Cycle Assessment and Fuzzy Synthetic Evaluation Approach’, Science of the Total Environment, vol. 845, Nov. 2022. [CrossRef]
64. Y. Xu, J. F. Y. Yeung, A. P. C. Chan, D. W. M. Chan, S. Q. Wang, and Y. Ke, ‘Developing a Risk Assessment Model for PPP projects in China-A Fuzzy Synthetic Evaluation Approach’, Autom Constr, vol. 19, no. 7, pp. 929–943, Nov. 2010. [CrossRef]
65. M. Akter et al., ‘Risk Assessment Based on Fuzzy Synthetic Evaluation Method’, Science of the Total Environment, vol. 658, pp. 818–829, Mar. 2019. [CrossRef]
66. X. Zhao, B. G. Hwang, and Y. Gao, ‘A Fuzzy Synthetic Evaluation Approach for Risk Assessment: A Case of Singapore’s Green Projects’, J Clean Prod, vol. 115, pp. 203–213, Mar. 2016. [CrossRef]
67. Yager R.R., ‘On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decision Making’, IEEE Trans Syst Man Cybern, vol. 18, no. 1, pp. 183–190, 1988. [CrossRef]