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Article
Publication date: 25 February 2014

Dawid J. D'Melo, Anagha S. Sabnis, Mohan A. Shenoy and Mukesh S. Kathalewar

The purpose of this paper is to evaluate the efficiency of acrylated guar gum (AGG) as an additive in alkyd resin for improved mechanical properties and to optimize the…

Abstract

Purpose

The purpose of this paper is to evaluate the efficiency of acrylated guar gum (AGG) as an additive in alkyd resin for improved mechanical properties and to optimize the results of such an addition.

Design/methodology/approach

For studying the effect of AGG on coating properties, guar gum was modified to various degrees of esterification and various compositions of alkyd systems were made by incorporating different concentrations of AGG. The mechanical and solvent absorption of the unmodified and modified alkyd systems were characterized.

Findings

The incorporation of AGG into alkyd coating showed significant improvement of mechanical properties over the unmodified one. The modification caused an additional crosslink site through its unsaturation which led to increased crosslink density without phase separation of additive from the alkyd system which was confirmed by SEM scans.

Research limitations/implications

The reactive additive, AGG used in the present study was synthesised using acryloyl chloride. Besides, it could also be synthesised from methacryloyl chloride and the effect of methyl substitution on water and solvent absorption could be studied.

Practical implications

The method developed provided a simple and practical solution to improving the mechanical properties of alkyd coatings.

Originality/value

The method for enhancing mechanical properties of cured alkyd system was novel and could find numerous applications in surface coatings.

Details

Pigment & Resin Technology, vol. 43 no. 2
Type: Research Article
ISSN: 0369-9420

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Article
Publication date: 11 April 2016

Yanzhen Wang, Zhongwei Yin, Dan Jiang, Gengyuan Gao and Xiuli Zhang

Water lubrication is significant for its environmental friendliness. Composite journal bearing is liable to deform for the huge pressure of water film. This paper aims to…

Abstract

Purpose

Water lubrication is significant for its environmental friendliness. Composite journal bearing is liable to deform for the huge pressure of water film. This paper aims to study the influence of elastic deformation on how lubrication functions in water-lubricated journal bearings and to provide references for designing composite journal bearings.

Design/methodology/approach

The combination of computational fluid dynamics and fluid-structure interaction is adopted in this paper to study the lubrication performance of water-lubricated compliant journal bearings. The influences of elasticity modulus and Poisson’s ratio on load-carrying capacity and elastic deformation are studied for different rotational speeds. Predictions in this work are compared with the published experimental results, and the present work agrees well with the experimental results.

Findings

A reference whether elastic deformation should be considered for composite journal bearings is proposed under different working conditions. Besides, a reference to determine water-lubricated plain journal bearings dimensions under different loads and rotational speeds is developed with the effect of both elastic deformation and cavitation being accounted.

Originality/value

The present research provides references as to whether elastic deformation should be considered in operation and to determine compliant journal bearings’ dimensions in the design process.

Details

Industrial Lubrication and Tribology, vol. 68 no. 3
Type: Research Article
ISSN: 0036-8792

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Article
Publication date: 20 August 2018

Laouni Djafri, Djamel Amar Bensaber and Reda Adjoudj

This paper aims to solve the problems of big data analytics for prediction including volume, veracity and velocity by improving the prediction result to an acceptable…

Abstract

Purpose

This paper aims to solve the problems of big data analytics for prediction including volume, veracity and velocity by improving the prediction result to an acceptable level and in the shortest possible time.

Design/methodology/approach

This paper is divided into two parts. The first one is to improve the result of the prediction. In this part, two ideas are proposed: the double pruning enhanced random forest algorithm and extracting a shared learning base from the stratified random sampling method to obtain a representative learning base of all original data. The second part proposes to design a distributed architecture supported by new technologies solutions, which in turn works in a coherent and efficient way with the sampling strategy under the supervision of the Map-Reduce algorithm.

Findings

The representative learning base obtained by the integration of two learning bases, the partial base and the shared base, presents an excellent representation of the original data set and gives very good results of the Big Data predictive analytics. Furthermore, these results were supported by the improved random forests supervised learning method, which played a key role in this context.

Originality/value

All companies are concerned, especially those with large amounts of information and want to screen them to improve their knowledge for the customer and optimize their campaigns.

Details

Information Discovery and Delivery, vol. 46 no. 3
Type: Research Article
ISSN: 2398-6247

Keywords

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Article
Publication date: 21 November 2018

Kiran Ahuja and Arun Khosla

This paper aims to focus on data analytic tools and integrated data analyzing approaches used on smart energy meters (SEMs). Furthermore, while observing the diverse…

Abstract

Purpose

This paper aims to focus on data analytic tools and integrated data analyzing approaches used on smart energy meters (SEMs). Furthermore, while observing the diverse techniques and frameworks of data analysis of SEM, the authors propose a novel framework for SEM by using gamification approach for enhancing the involvement of consumers to conserve energy and improve efficiency.

Design/methodology/approach

A few research strategies have been accounted for analyzing the raw data, yet at the same time, a considerable measure of work should be done in making these commercially reasonable. Data analytic tools and integrated data analyzing approaches are used on SEMs. Furthermore, while observing the diverse techniques and frameworks of data analysis of SEM, the authors propose a novel framework for SEM by using gamification approach for enhancing the involvement of consumers to conserve energy and improve efficiency. Advantages of SEM’s are additionally discussed for inspiring consumers, utilities and their respective partners.

Findings

Consumers, utilities and researchers can also take benefit of the recommended framework by planning their routine activities and enjoying rewards offered by gamification approach. Through gamification, consumers’ commitment enhances, and it changes their less manageable conduct on an intentional premise. The practical implementation of such approaches showed the improved energy efficiency as a consequence.

Details

International Journal of Energy Sector Management, vol. 13 no. 2
Type: Research Article
ISSN: 1750-6220

Keywords

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Article
Publication date: 19 September 2019

Chengzhi Zhang, Tiantian Tong and Yi Bu

Websites have their own features in aspect preference (e.g. the relative importance platforms place on product aspects in product evaluation). The purpose of this paper is…

Abstract

Purpose

Websites have their own features in aspect preference (e.g. the relative importance platforms place on product aspects in product evaluation). The purpose of this paper is to capture characteristics of different book reviews on aspect preferences by opinion mining techniques.

Design/methodology/approach

The authors employ two indicators for identifying aspect preferences, and propose a method for quantifying overall differences of reviews on aspect preferences through three dimensions: aspect awareness, aspect satisfaction and comprehensive value.

Findings

The results show that book reviews on e-commerce websites contain information about external aspects of a book (e.g. hardcover), while those on social network websites pay more attention to content-related aspects of the book (e.g. stories). These results indicate that aspect preferences of reviews vary from platforms and make it hard to evaluate book comprehensively based on single-source data. Online book reviews from a wide range of sources can assess book impact from multiple perspectives and dimensions.

Practical implications

In order to illustrate the value of the authors’ method, the authors show book impact assessment based on multi-source data as an application of these difference analyses. Furthermore, the authors present an example of a book promotion to provide customized marketing services for different user clusters.

Originality/value

This study investigates the influence of different data sources on book evaluation from the content of book reviews. The authors also showcase potential applications of these analyses in book impact assessment.

Details

Online Information Review, vol. 43 no. 7
Type: Research Article
ISSN: 1468-4527

Keywords

Content available
Article
Publication date: 31 July 2020

Omar Alqaryouti, Nur Siyam, Azza Abdel Monem and Khaled Shaalan

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help…

Abstract

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

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Article
Publication date: 19 February 2018

Qiujun Lan, Haojie Ma and Gang Li

Sentiment identification of Chinese text faces many challenges, such as requiring complex preprocessing steps, preparing various word dictionaries carefully and dealing…

Abstract

Purpose

Sentiment identification of Chinese text faces many challenges, such as requiring complex preprocessing steps, preparing various word dictionaries carefully and dealing with a lot of informal expressions, which lead to high computational complexity.

Design/methodology/approach

A method based on Chinese characters instead of words is proposed. This method represents the text into a fixed length vector and introduces the chi-square statistic to measure the categorical sentiment score of a Chinese character. Based on these, the sentiment identification could be accomplished through four main steps.

Findings

Experiments on corpus with various themes indicate that the performance of proposed method is a little bit worse than existing Chinese words-based methods on most texts, but with improved performance on short and informal texts. Especially, the computation complexity of the proposed method is far better than words-based methods.

Originality/value

The proposed method exploits the property of Chinese characters being a linguistic unit with semantic information. Contrasting to word-based methods, the computational efficiency of this method is significantly improved at slight loss of accuracy. It is more sententious and cuts off the problems resulted from preparing predefined dictionaries and various data preprocessing.

Details

Information Discovery and Delivery, vol. 46 no. 1
Type: Research Article
ISSN: 2398-6247

Keywords

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Article
Publication date: 3 January 2018

Lei La, Shuyan Cao and Liangjuan Qin

As a foundational issue of social mining, sentiment classification suffered from a lack of unlabeled data. To enhance accuracy of classification with few labeled data…

Abstract

Purpose

As a foundational issue of social mining, sentiment classification suffered from a lack of unlabeled data. To enhance accuracy of classification with few labeled data, many semi-supervised algorithms had been proposed. These algorithms improved the classification performance when the labeled data are insufficient. However, precision and efficiency are difficult to be ensured at the same time in many semi-supervised methods. This paper aims to present a novel method for using unlabeled data in a more accurate and more efficient way.

Design/methodology/approach

First, the authors designed a boosting-based method for unlabeled data selection. The improved boosting-based method can choose unlabeled data which have the same distribution with the labeled data. The authors then proposed a novel strategy which can combine weak classifiers into strong classifiers that are more rational. Finally, a semi-supervised sentiment classification algorithm is given.

Findings

Experimental results demonstrate that the novel algorithm can achieve really high accuracy with low time consumption. It is helpful for achieving high-performance social network-related applications.

Research limitations/implications

The novel method needs a small labeled data set for semi-supervised learning. Maybe someday the authors can improve it to an unsupervised method.

Practical implications

The mentioned method can be used in text mining, image classification, audio processing and so on, and also in an unstructured data mining-related field. Overcome the problem of insufficient labeled data and achieve high precision using fewer computational time.

Social implications

Sentiment mining has wide applications in public opinion management, public security, market analysis, social network and related fields. Sentiment classification is the basis of sentiment mining.

Originality/value

According to what the authors have been informed, it is the first time transfer learning be introduced to AdaBoost for semi-supervised learning. Moreover, the improved AdaBoost uses a totally new mechanism for weighting.

Details

Kybernetes, vol. 47 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 25 October 2018

Shrawan Kumar Trivedi, Shubhamoy Dey and Anil Kumar

Sentiment analysis and opinion mining are emerging areas of research for analyzing Web data and capturing users’ sentiments. This research aims to present sentiment…

Abstract

Purpose

Sentiment analysis and opinion mining are emerging areas of research for analyzing Web data and capturing users’ sentiments. This research aims to present sentiment analysis of an Indian movie review corpus using natural language processing and various machine learning classifiers.

Design/methodology/approach

In this paper, a comparative study between three machine learning classifiers (Bayesian, naïve Bayesian and support vector machine [SVM]) was performed. All the classifiers were trained on the words/features of the corpus extracted, using five different feature selection algorithms (Chi-square, info-gain, gain ratio, one-R and relief-F [RF] attributes), and a comparative study was performed between them. The classifiers and feature selection approaches were evaluated using different metrics (F-value, false-positive [FP] rate and training time).

Findings

The results of this study show that, for the maximum number of features, the RF feature selection approach was found to be the best, with better F-values, a low FP rate and less time needed to train the classifiers, whereas for the least number of features, one-R was better than RF. When the evaluation was performed for machine learning classifiers, SVM was found to be superior, although the Bayesian classifier was comparable with SVM.

Originality/value

This is a novel research where Indian review data were collected and then a classification model for sentiment polarity (positive/negative) was constructed.

Details

The Electronic Library, vol. 36 no. 4
Type: Research Article
ISSN: 0264-0473

Keywords

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Article
Publication date: 29 October 2018

Shrawan Kumar Trivedi and Shubhamoy Dey

To be sustainable and competitive in the current business environment, it is useful to understand users’ sentiment towards products and services. This critical task can be…

Abstract

Purpose

To be sustainable and competitive in the current business environment, it is useful to understand users’ sentiment towards products and services. This critical task can be achieved via natural language processing and machine learning classifiers. This paper aims to propose a novel probabilistic committee selection classifier (PCC) to analyse and classify the sentiment polarities of movie reviews.

Design/methodology/approach

An Indian movie review corpus is assembled for this study. Another publicly available movie review polarity corpus is also involved with regard to validating the results. The greedy stepwise search method is used to extract the features/words of the reviews. The performance of the proposed classifier is measured using different metrics, such as F-measure, false positive rate, receiver operating characteristic (ROC) curve and training time. Further, the proposed classifier is compared with other popular machine-learning classifiers, such as Bayesian, Naïve Bayes, Decision Tree (J48), Support Vector Machine and Random Forest.

Findings

The results of this study show that the proposed classifier is good at predicting the positive or negative polarity of movie reviews. Its performance accuracy and the value of the ROC curve of the PCC is found to be the most suitable of all other classifiers tested in this study. This classifier is also found to be efficient at identifying positive sentiments of reviews, where it gives low false positive rates for both the Indian Movie Review and Review Polarity corpora used in this study. The training time of the proposed classifier is found to be slightly higher than that of Bayesian, Naïve Bayes and J48.

Research limitations/implications

Only movie review sentiments written in English are considered. In addition, the proposed committee selection classifier is prepared only using the committee of probabilistic classifiers; however, other classifier committees can also be built, tested and compared with the present experiment scenario.

Practical implications

In this paper, a novel probabilistic approach is proposed and used for classifying movie reviews, and is found to be highly effective in comparison with other state-of-the-art classifiers. This classifier may be tested for different applications and may provide new insights for developers and researchers.

Social implications

The proposed PCC may be used to classify different product reviews, and hence may be beneficial to organizations to justify users’ reviews about specific products or services. By using authentic positive and negative sentiments of users, the credibility of the specific product, service or event may be enhanced. PCC may also be applied to other applications, such as spam detection, blog mining, news mining and various other data-mining applications.

Originality/value

The constructed PCC is novel and was tested on Indian movie review data.

1 – 10 of 12