Purpose Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study. Design/methodology/approach This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews. Findings The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff. Originality/value This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

A critical survey of the abundant literature on environmental remediation and water treatment using metallic iron (Fe0) as reactive agent raises two major concerns: (i) the peculiar properties of the used materials are not properly considered and characterized, and, (ii) the literature review in individual publications is very selective, thereby excluding some fundamental principles. Fe0 specimens for water treatment are typically small in size. Before the advent of this technology and its application for environmental remediation, such small Fe0 particles have never been allowed to freely corrode for the long-term spanning several years. As concerning the selective literature review, the root cause is that Fe0 was considered as a (strong) reducing agent under environmental conditions. Subsequent interpretation of research results was mainly directed at supporting this mistaken view. The net result is that, within three decades, the Fe0 research community has developed itself to a sort of modern knowledge system. This communication is a further attempt to bring Fe0 research back to the highway of mainstream corrosion science, where the fundamentals of Fe0 technology are rooted. The inherent errors of selected approaches, currently considered as countermeasures to address the inherent limitations of the Fe0 technology are demonstrated. The misuse of the terms “reactivity”, and “efficiency”, and adsorption kinetics and isotherm models for Fe0 systems is also elucidated. The immense importance of Fe0/H2O systems in solving the long-lasting issue of universal safe drinking water provision and wastewater treatment calls for a science-based system design.

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Stretchable strain sensors of conductive hydrogels have been widely used in wearable devices and soft robotics. These applications have posed combinational requirements for the hydrogels: high fatigue resistance, low hysteresis,...

Travel-time based hydraulic tomography is a promising method to characterize heterogeneity of porous-fractured aquifers. However, there is inevitable noise in field-scale experimental data and many hydraulic signal travel times, which are derived from the pumping test’s first groundwater level derivative drawdown curves and are strongly influenced by noise. The required data processing is thus quite time consuming and often not accurate enough. Therefore, an effective and accurate de-noising method is required for travel time inversion data processing. In this study, a series of hydraulic tomography experiments were conducted at a porous-fractured aquifer test site in Goettingen, Germany. A numerical model was built according to the site’s field conditions and tested based on diagnostic curve analyses of the field experimental data. Gaussian white noise was then added to the model’s calculated pumping test drawdown data to simulate the real noise in the field. Afterward, different de-noising methods were applied to remove it. This study has proven the superiority of the wavelet de-noising approach compared with several other filters. A wavelet de-noising method with calibrated mother wavelet type, de-noising level, and wavelet level was then determined to obtain the most accurate travel time values. Finally, using this most suitable de-noising method, the experimental hydraulic tomography travel time values were calculated from the de-noised data. The travel time inversion based on this de-noised data has shown results consistent with previous work at the test site.

Sentiment analysis or opinion mining is the key to natural language processing for the extraction of useful information from the text documents of numerous sources. Several different techniques, i.e., simple rule-based to lexicon-based and more sophisticated machine learning algorithms, have been widely used with different classifiers to get the factual analysis of sentiment. However, lexicon-based sentiment classification is still suffering from low accuracies, mainly due to the deficiency of domain-oriented competitive dictionaries. Similarly, machine learning-based sentiment is also tackling the accuracy constraints because of feature ambiguity from social data. One of the best ways to deal with the accuracy issue is to select the best feature-set and reduce the volume of the feature. This paper proposes a method (namely, GAWA) for feature selection by utilizing the Wrapper Approaches (WA) to select the premier features and the Genetic Algorithm (GA) to reduce the size of the premier features. The novelty of this work is the modified fitness function of heuristic GA to compute the optimal features by reducing the redundancy for better accuracy. This work aims to present a comprehensive model of hybrid sentiment by using the proposed method, GAWA. It will be valued in developing a new approach for the selection of feature-set with a better accuracy level. The experiments revealed that these techniques could reduce the feature-set upto 61.95% without negotiating the accuracy level. The new optimal feature sets enhanced the efficiency of the Naïve Bayes algorithm up to 92%. This work is compared with the conventional method of feature selection and concluded the 11% better accuracy than PCA and 8% better than PSO. Furthermore, the results are compared with the literature work and found that the proposed method outperformed the previous research.

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Granular metallic iron (gFe0) materials have been widely used for eliminating a wide range of pollutants from aqueous solutions over the past three decades. However, the intrinsic reactivity of gFe0 is rarely evaluated and existing methods for such evaluations have not been standardized. The aim of the present study was to develop a simple spectrophotometric method to characterize the intrinsic reactivity of gFe0 based on the extent of iron dissolution in an ascorbic acid (AA—0.002 M or 2 mM) solution. A modification of the ethylenediaminetetraacetic acid method (EDTA method) is suggested for this purpose. Being an excellent chelating agent for FeII and a reducing agent for FeIII, AA sustains the oxidative dissolution of Fe0 and the reductive dissolution of FeIII oxides from gFe0 specimens. In other words, Fe0 dissolution to FeII ions is promoted while the further oxidation to FeIII ions is blocked. Thus, unlike the EDTA method that promotes Fe0 oxidation to FeIII ions, the AA method promotes only the formation of FeII species, despite the presence of dissolved O2. The AA test is more accurate than the EDTA test and is considerably less expensive. Eight selected gFe0 specimens (ZVI1 through ZVI8) with established diversity in intrinsic reactivity were tested in parallel batch experiments (for 6 days) and three of these specimens (ZVI1, ZVI3, ZVI5) were further tested for iron leaching in column experiments (for 150 days). Results confirmed the better suitability (e.g., accuracy in assessing Fe0 dissolution) of the AA test relative to the EDTA test as a powerful screening tool to select materials for various field applications. Thus, the AA test should be routinely used to characterize and rationalize the selection of gFe0 in individual studies.