Ranking Tourist Attractions through Online Reviews: A Novel Method with Intuitionistic and Hesitant Fuzzy Information Based on Sentiment Analysis

Machine learning applications in preventive healthcare: A systematic literature review on predictive analytics of disease comorbidity from multiple perspectives

Artificial intelligence is constantly revolutionizing biomedical research and healthcare management. Disease comorbidity is a major threat to the quality of life for susceptible groups, especially middle-aged and elderly patients. The presence of multiple chronic diseases makes precision diagnosis challenging to realize and imposes a heavy burden on the healthcare system and economy. Given an enormous amount of accumulated health data, machine learning techniques show their capability in handling this puzzle. The present study conducts a review to uncover current research efforts in applying these methods to understanding comorbidity mechanisms and making clinical predictions considering these complex patterns. A descriptive metadata analysis of 791 unique publications aims to capture the overall research progression between January 2012 and June 2023. To delve into comorbidity-focused research, 61 of these scientific papers are systematically assessed. Four predictive analytics of tasks are detected: disease comorbidity data extraction, clustering, network, and risk prediction. It is observed that some machine learning-driven applications address inherent data deficiencies in healthcare datasets and provide a model interpretation that identifies significant risk factors of comorbidity development. Based on insights, both technical and practical, gained from relevant literature, this study intends to guide future interests in comorbidity research and draw conclusions about chronic disease prevention and diagnosis with managerial implications.

Performance evaluation of machine learning models on large dataset of android applications reviews

With an ever-increasing number of mobile users, the development of mobile applications (apps) has become a potential market during the past decade. Billions of users download mobile apps for divergent use from Google Play Store, fulfill tasks and leave comments about their experience. Such reviews are replete with a variety of feedback that serves as a guide for the improvement of existing apps and intuition for novel mobile apps. However, application reviews are challenging and very broad to approach. Such reviews, when segregated into different classes guide the user in the selection of suitable apps. This study proposes a framework for analyzing the sentiment of reviews for apps of eight different categories like shopping, sports, casual, etc. A large dataset is scrapped comprising 251661 user reviews with the help of 'Regular Expression' and 'Beautiful Soup'. The framework follows the use of different machine learning models along with the term frequency-inverse document frequency (TF-IDF) for feature extraction. Extensive experiments are performed using preprocessing steps, as well as, the stats feature of app reviews to evaluate the performance of the models. Results indicate that combining the stats feature with TF-IDF shows better performance and the support vector machine obtains the highest accuracy. Experimental results can potentially be used by other researchers to select appropriate models for the analysis of app reviews. In addition, the provided dataset is large, diverse, and balanced with eight categories and 59 app reviews and provides the opportunity to analyze reviews using state-of-the-art approaches.

Rule Based Fuzzy Computing Approach on Self-Supervised Sentiment Polarity Classification with Word Sense Disambiguation in Machine Translation for Hindi Language

With the increasing globalization communication among the people of diverse cultural backgrounds is also taking place to a very large extent in the present era. Issues like language diversity in various parts of the world can lead to hindrance in communication. The usage of social media and user-generated material has grown at an exponential rate and existing supervised sentiment polarity classification techniques need labelling for the training dataset. In this study, two problems have been analyzed First, sentiment analysis of twitter dataset and sense disambiguation of morphologically rich Hindi language. A rule-based fuzzy logics-based system for self-supervised sentiment classification was used to compute and analyze the self-supervised or completely unsupervised sentiment categorization of a social-media dataset using three types of lexicons. The combination of fuzzy with three different types of lexicons gives sentiment analysis a new path. The unsupervised fuzzy rules integrate the fuzziness of both negative as well as positive scores, fuzzy logic-based systems can cope with ambiguity and vagueness. The fuzzy-system uses an unsupervised/self-supervised fuzzy rule-based technique to identify text using natural language processing (NLP) and sense of word. We compared the results of fuzzy rule based self-supervised sentiment classification by using three types of lexicons on five different datasets, with unsupervised as well as supervised sentiment classification techniques. Second, using cross-lingual sense embedding rather than cross-lingual word embedding resolves the ambiguity issue. The word sense embeddings are produced for the source languages to learn multiple or various senses of the words. Different evaluation metrics depict an improved performance for English-Hindi language.

Data-driven product ranking: A hybrid ranking approach

The sudden COVID-19 epidemic has caused consumers to gradually switch to online shopping, the increasing number of online consumer reviews (OCR) on Web 2.0 sites has made it difficult for consumers and merchants to make decisions by analyzing OCR. Much of the current literature on ranking products based on OCR ignores neutral reviews in OCR, evaluates mostly given criteria and ignores consumers’ own purchasing preferences, or ranks based on star ratings alone. This study aims to propose a new decision support framework for the evaluation and selection of alternative products based on OCR. The decision support framework mainly includes three parts: 1) Data preprocessing: using Python to capture online consumer comments for data cleaning and preprocessing, and extracting key features as evaluation criteria; 2) Sentiment analysis: using Naive Bayes to analyze the sentiment of OCR, and using intuitionistic fuzzy sets to describe the emotion score; 3) Benchmark analysis: a new IFMBWM-DEA model considering the preference of decision makers is proposed to calculate the efficiency score of alternative schemes and rank them according to the efficiency score. Then, the OCR of 15 laptops crawled from JD.com platform is used to prove the usefulness and applicability of the proposed decision support framework in two aspects: on the one hand, the comparison of whether the preference of decision makers is considered, and on the other hand, the comparison with the existing ranking methods. The comparison also proves that the proposed method is more realistic, the recommendations are more scientific and the complexity of the decision is reduced.