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The need of tools for content analysis, information extraction and retrieval of multimedia objects in their native form is strongly emphasized into the judicial domain: digital videos represent a fundamental informative source of events occurring during judicial proceedings that should be stored, organized and retrieved in short time and with low cost. This paper seeks to address these issues.

In this context the JUMAS system, stem from the homonymous European Project (www.jumasproject.eu), takes up the challenge of exploiting semantics and machine learning techniques towards a better usability of multimedia judicial folders.

In this paper one of the most challenging issues addressed by the JUMAS project is described: extracting meaningful abstracts of given judicial debates in order to efficiently access salient contents. In particular, the authors present an ontology enhanced multimedia summarization environment able to derive a synthetic representation of judicial media contents by a limited loss of meaningful information while overcoming the information overload problem.

The adoption of ontology‐based query expansion has made it possible to improve the performance of multimedia summarization algorithms with respect to the traditional approaches based on statistics. The effectiveness of the proposed approach has been evaluated on real media contents, highlighting a good potential for extracting key events in the challenging area of judicial proceedings.

This paper aims to build a legal intelligent auxiliary discretionary system for predicting the penalty and damage compensation values. After extensively considering current the characteristics of the current Chinese legal system, a practical legal intelligent auxiliary discretionary system based on genetic algorithm-backpropagation (GA-BP) neural network (NN) is proposed herein.

An experiment is designed to analyze cases involving mental anguish compensation in medical disputes, and a Chinese legal intelligent auxiliary discretionary adviser system is built based on a GA-BP NN. Because BP neural networks perform well for nonlinear problems and GAs can improve their ability to find optimal values, and accelerate their convergence, a combined GA–BP algorithm is used. In addition, an ontology is used to reduce the semantic ambiguities and extract the implied semantic information.

We confirm that a case-based legal intelligent auxiliary discretionary adviser system based on a GA-BP NN and ontology techniques has good performance in prediction. By predicting the mental anguish compensation values, the legal intelligent auxiliary discretionary adviser system can help judges to handle cases more quickly and ordinary people to discover the suggested compensation or penalty. In contrast to BP NN or SVM, the result seems more close to the actual compensation rate.

Recently, smart court has been developed in China; the purpose of which is to build the legal advice system for improving judicial justice and reducing differences in sentencing. A practical legal advice system is an urgent requirement for the judiciary.

This paper presents a study of a case-based legal intelligent auxiliary discretionary adviser system based on a GA-BP NN and ontology techniques. The findings offer advice to optimize legal intelligent auxiliary discretionary adviser systems for mental anguish compensation in medical disputes.

This paper aims to address the challenge of analysing the features of negative sentiment tweets. The method adopted in this paper elucidates the classification of social network documents and paves the way for sentiment analysis of tweets in further research.

This study classifies negative tweets and analyses their features.

Through negative tweet content analysis, tweets are divided into ten topics. Many related words and negative words were found. Some indicators of negative word use could reflect the degree to which users release negative emotions: part of speech, the density and frequency of negative words and negative word distribution. Furthermore, the distribution of negative words obeys Zipf’s law.

This study manually analysed only a small sample of negative tweets.

The research explored how many categories of negative sentiment tweets there are on Twitter. Related words are helpful to construct an ontology of tweets, which helps people with information retrieval in a fixed research area. The analysis of extracted negative words determined the features of negative tweets, which is useful to detect the polarity of tweets by machine learning method.

The research provides an initial exploration of a negative document classification method and classifies the negative tweets into ten topics. By analysing the features of negative tweets, related words, negative words, the density of negative words, etc. are presented. This work is the first step to extend Plutchik’s emotion wheel theory into social media data analysis by constructing filed specific thesauri, referred to as local sentimental thesauri.

The purpose of this paper is to find a representative subset from large-scale online reviews for consumers. The subset is significantly small in size, but covers the majority amount of information in the original reviews and contains little redundant information.

A heuristic approach named RewSel is proposed to successively select representatives until the number of representatives meets the requirement. To reveal the advantages of the approach, extensive data experiments and a user study are conducted on real data.

The proposed approach has the advantage over the benchmarks in terms of coverage and redundancy. People show preference to the representative subsets provided by RewSel. The proposed approach also has good scalability, and is more adaptive to big data applications.

The paper contributes to the literature of review selection, by proposing a heuristic approach which achieves both high coverage and low redundancy. This study can be applied as the basis for conducting further analysis of large-scale online reviews.

The proposed approach offers a novel way to select a representative subset of online reviews to facilitate consumer decision making. It can also enhance the existing information retrieval system to provide representative information to users rather than a large amount of results.

The proposed approach finds the representative subset by adopting the concept of relative entropy and sentiment analysis methods. Compared with state-of-the-art approaches, it offers a more effective and efficient way for users to handle a large amount of online information.

The purpose of this paper is to explore legal information requirements of the legal community in India for a proposed online legal information system tailored to the Indian environment.

A needs assessment survey was conducted using a structured questionnaire circulated among 750 respondents from eight institutions in Delhi. A total of 397 filled-in questionnaires were personally collected by the investigator, showing a response rate of 52.9 per cent. All these questionnaires were selected for analysis and interpretation of data. Responses to 45 questions were analyzed and presented in the form of tables and figures using various statistical techniques.

The findings of the study show that majority of the respondents felt they faced a number of problems in using online legal resources such as accessibility of legal information in legal resources, lack of online help features, description of legal information sources, search screen too confusing and poor website design. In addition, respondents highlighted that access instructions on the online resources are not clear. Lack of expertise and insufficient knowledge of information and communication technology in using legal databases are also major hurdles. Majority of the respondents are somewhat satisfied in using open-access and commercial legal information resources and not aware of open-access resources in the field of law. Model online legal information system (OLIS) was designed and developed based on the findings drawn in the needs assessment survey to empower the common man with legal resources at no cost, and foster research in the field of law.

The model OLIS contains only a sample collection. It is expected that the proposed system will be implemented on a wider scale, so that lawyers, research scholars and common people can benefit.

The findings of the study will help libraries to subscribe to legal information resources and to design robust OLIS suitable in the Indian context. It is anticipated that the needs assessment survey of OLIS will help government agencies and law libraries to understand the problems of the legal fraternity in accessing legal information.

The present study designed a model OLIS for the Indian environment (www.olisindia.in) to aid the legal community in India, enabling them to save their valuable time. This system will help and foster interdisciplinary research learning and can be used as a tool for learning the basic concepts, as well as help deliberate on the emerging areas in the field of law.

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.

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.

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.

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.

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.

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.

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

Machine learning algorithms are useful to effectively analyse, and therefore automatically classify online reviews. The purpose of this paper is to demonstrate a novel text-mining framework and its potential for use in the classification of unstructured hotel reviews.

Well-known data mining methods (i.e. boosted decision trees (BDT), classification and regression trees (C&RT) and random forests (RF)) in conjunction with incorporating five-fold cross-validation are used to predict the star rating of the hotel reviews. To achieve this goal, extracted features are used to create a composite variable (CV) to deploy into machine learning algorithms as the main feature (variable) during the learning process.

BDT outperformed the other alternatives in the exact accuracy rate (EAR) and multi-class accuracy rate (MCAR) by reaching the accuracy rates of 0.66 and 0.899, respectively. Moreover, phrases such as “clean”, “friendly”, “nice”, “perfect” and “love” are shown to be associated with four and five stars, whereas, phrases such as “horrible”, “never”, “terrible” and “worst” are shown to be associated with one and two-star hotels, as it would be the intuitive expectation.

To the best of the knowledge, there is no study in the existent literature, which synthesizes the knowledge obtained from individual features and uses them to create a single composite variable that is powerful enough to predict the star rates of the user-generated reviews. This study believes that the proposed method also provides policymakers with a unique window in the thoughts and opinions of individual users, which may be used to augment the current decision-making process.

This paper aims to summarize the current state of research on risk, crisis and disaster management in the generic field, and in tourism and hospitality. It identifies key themes and compares the main topics studied in both the tourism and hospitality management and marketing literature.

A narrative (thematic) review and synthesis was completed based on articles published in the top 20 tourism and hospitality management journals from 2011 to March 2021. A review was conducted of the generic literature from 2016 to 2020.

From 210 papers reviewed, only 47 are in the hospitality field. The authors found that 80% of papers were empirical with slightly more quantitative papers produced. The majority of the papers focused on crises. Three key themes were found from the review and future research proposed to address gaps based on these findings and a review of 26 papers from the generic risk, crisis and disaster management field.

Research is required into planning and preparedness, not just response and recovery to crises and disasters. Future research should consider hospitality rather than tourism, particularly focusing attention outside of the accommodation sector. Hospitality studies also need to go beyond the micro-organizational level to include more meso- and macro-level studies.

The review provides a number of future research directions for tourism and hospitality research in the field. The paper provides a comprehensive multi-dimensional framework to synthesize studies and identifies research gaps. It also provides recommendations on methodologies required to progress these research directions. Research in this field is likely to grow because of the impact of COVID-19.

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.

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).

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.

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

The Web 3.0 has been hugely enabled by smartphones and new generation mobile applications. With the growing adoption of smartphones, the use of mobile applications has grown exponentially and so has the development of mobile applications. This study is an attempt to understand the issues and challenges faced in the mobile applications domain using discussions made on Twitter based on mining of user generated content.

The study uses 89,908 unique tweets to understand the nature of the discussions. These tweets are analyzed using descriptive, content and network analysis. Further using transaction cost economics, the findings are reviewed to develop practice insights about the ecosystem.

Findings indicate that the discussions are mostly skewed toward a positive polarity and positive user experiences. The tweeters are predominantly application developers who are interacting more with marketers and less with individual users.

Most of these applications are for individual use (B2C) and not for enterprise usage. There are very few individual users who contribute to these discussions. The predominant users are application reviewers or bloggers of review websites who use the recently developed applications and discuss their thoughts on the same.

The results may be useful in varied domains which are planning to expand their reach to a larger audience using mobile applications and for marketers who primarily focus on promotional content.

The domain of mobile applications on social media is still restricted to promotions and digital marketing and may solely be used for the purpose of link building by application developers. As such, the discussions could provide inputs towards mobile phone manufacturers and ecosystem providers on what are the real issues these communities are facing while developing these applications.

The study uses mixed research methodology for mining experiences in the domain of mobile application developers using social media analytics and transaction cost economics. The discussion on the findings provides inputs for policy-making and possible intervention areas.