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This paper aims to present a new term weighting approach for text classification as a text mining task. The original method, neutrosophic term frequency – inverse term frequency (NTF-IDF), is an extended version of the popular fuzzy TF-IDF (FTF-IDF) and uses the neutrosophic reasoning to analyze and generate weights for terms in natural languages. The paper also propose a comparative study between the popular FTF-IDF and NTF-IDF and their impacts on different machine learning (ML) classifiers for document categorization goals.

After preprocessing textual data, the original Neutrosophic TF-IDF applies the neutrosophic inference system (NIS) to produce weights for terms representing a document. Using the local frequency TF, global frequency IDF and text N's length as NIS inputs, this study generate two neutrosophic weights for a given term. The first measure provides information on the relevance degree for a word, and the second one represents their ambiguity degree. Next, the Zhang combination function is applied to combine neutrosophic weights outputs and present the final term weight, inserted in the document's representative vector. To analyze the NTF-IDF impact on the classification phase, this study uses a set of ML algorithms.

Practicing the neutrosophic logic (NL) characteristics, the authors have been able to study the ambiguity of the terms and their degree of relevance to represent a document. NL's choice has proven its effectiveness in defining significant text vectorization weights, especially for text classification tasks. The experimentation part demonstrates that the new method positively impacts the categorization. Moreover, the adopted system's recognition rate is higher than 91%, an accuracy score not attained using the FTF-IDF. Also, using benchmarked data sets, in different text mining fields, and many ML classifiers, i.e. SVM and Feed-Forward Network, and applying the proposed term scores NTF-IDF improves the accuracy by 10%.

The novelty of this paper lies in two aspects. First, a new term weighting method, which uses the term frequencies as components to define the relevance and the ambiguity of term; second, the application of NL to infer weights is considered as an original model in this paper, which also aims to correct the shortcomings of the FTF-IDF which uses fuzzy logic and its drawbacks. The introduced technique was combined with different ML models to improve the accuracy and relevance of the obtained feature vectors to fed the classification mechanism.

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

This study aims to investigate the shopping behaviour of consumers, mainly in fashion apparels, and intends to understand consumer buying patterns in Indian context. The study was designed to determine the level of consumer's sense of belonging towards apparel shopping by applying the concept of self-congruence.

The study used variance-based partial least squares structural equational modelling (PLS-SEM) on a cross-sectional study conducted on 569 consumers. The study was conducted by using questionnaire to collect the responses from the central zone of India. The results support most of the projected hypotheses.

The study focused on the shopping behaviour of consumer such as self-congruence, impulse buying, hedonic values and consumer satisfaction. The results of the study highlight the association of constructs and analysed the mediation relation of hedonic and impulse buying constructs. The results revealed a positive association among the constructs and also found a partial mediation effect in their relation with constructs.

The findings are outcomes of an empirical study conducted in the fashion apparel industry of India based on the sample set of urban consumers. The study is restricted to the direct and indirect relationship of constructs. Further, research can examine by using moderating constructs like demographic factors (gender, age, income, etc.) and other shopping behaviours (like brand loyalty, brand love, brand attachment) for more clarity in results. Moreover, the study limited is with fashion apparel, whereas there are many categories in the fashion industry like accessories, perfumes, cosmetic products, footwear and also other products industry.

The study provided valuable inputs to the literature of marketing where self-congruence affects consumer shopping behaviour such as impulse buying, hedonic values and consumer satisfaction. The study proposes a practical approach that can help the marketing professionals and product developers to have a deep understanding about consumer shopping behaviour for facilitating consumer-oriented goods in the Indian fashion industry.

This is one of the first studies in the fashion industry to test the association of self-congruence with hedonic value and consumer satisfaction. This relation is not tested in context of fashion apparel. Additionally, this study also examined the mediating effect of hedonic value and impulse buying in relation with self-congruence and consumer satisfaction in the Indian context.