Search results

The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods.

A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal.

The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions.

Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.

An attempt is made to save the law of excluded middle in fuzzy set theory. This is done by mapping the sentences of axiomatic set theory to a Boolean algebra, prior to assigning numerical values. With a simple choice of Boolean algebra a probability interpretation emerges.

To provide better services to customers, especially immediate responses and 24/7 availability, businesses are implementing text-based automated conversational agents, i.e. chatbots on their social platforms and websites. Chatbots are required to not only provide customers with necessary consultancy and guidance but also communicate friendly and socially. Based on the cognitive fit theory, this study attempts to examine the role of chatbot as a decision aid and how the match between information presentation in forms of decisional guidance and communication style and the shopping task influences consumers' perceived cognitive fit and decision performance outcomes.

A 2 x 2 x 2 between subject online experiment was conducted to identify which kind of decisional guidance (suggestive and informative guidance) and communication style (task-oriented vs social-oriented style) are the most appropriate for each type of shopping task (searching vs browsing task).

The findings show that when customers interact with chatbots, they will perceive higher cognitive fit if the chatbots provide them with suggestive guidance and communicate in a friendly style especially when they perform a searching task.

This study is the first attempt to understand the role of chatbots as a decision aid to customers using the communicative language. This study also tries to explore the cognitive fit theory in a novel way, and we propose the information presentation in forms of communicative language rather than matrices, tables and graphs.

This study aims to explore the demographic and reconviction data of individuals who had engaged in an established Intensive Intervention and Risk Management Service, a community service within the offender personality disorder pathway (OPD).

Demographic information and reconviction data from the Police National Computer was accessed for all participants (n = 69) released to the service over a 10-year period (2008– 2018), 45 of who met criteria to be included in the study. Their intervention period spans custody to community, with an expected period of 2 years engagement upon release.

Participants in the service had experienced significant traumatic histories, with four or more adverse childhood experiences. Individuals had a mean of 17 convictions for violent and/or sexual offences and an average of 11 years in custody prior to release. The average length of intervention at Resettle in the community was almost 19 months, with an additional minimum of six months in-reach prior to release. A total of 60% of individuals were recalled back to custody at least once during their intervention, with this being more likely for those who had been convicted of a violent rather than sexual offence. Within the group who were reconvicted, the reconvictions were for less serious offences than the index offence, with only 16% being convicted of an indictable offence. Almost two-thirds (64.4%) did not receive a further conviction post-intervention in the follow-up period.

This follow-up study focused upon demographic and reconviction data from one established IIRMS. Although findings are not necessarily generalisable to other IIRMS and OPD pathway services, the demographic and reconviction data has important learning for how services may reflect upon engaging with individuals whose needs and risks had not previously been adequately met and managed upon release. This data are useful learning, for what may help individuals with complex needs upon release into the community after long sentences and how to best meet their needs. There are aspects of the Resettle IIRMS approach which could be applied to non-specialist services to encourage a holistic, compassionate and relational approach to reaching those with complex needs who pose significant risks to others.

This follow-up study has provided access to participants’ engagement with an established IIRMS. Although participants “opt in” to the service whilst in custody, engagement on release becomes a probation licence condition; a fixed boundary regarding attendance and engagement which, although enables robust risk management and reduces the likelihood of drop-out, also raises consideration about choice and control. The four day per week service provides an intensive intervention, for those with complex needs and limited prior experience of living safely (with minimal risk to self or others) in the community. Participants were previously offered little hope of release or effective support because of their risk, need, complexity and, in some cases, concern about whether their risk could be effectively managed. Although not a panacea for all, the results are suggestive of a service that is navigating the boundary between “care and control” to good effect for future resettlement and desistance.

The findings are important for service providers, commissioners and the public purse. The results are useful for the national development of IIRMS. The findings are also important for prisoners and people on probation to inform their decision-making regarding intervention choices and hope for what may be achievable.

To propose a comprehensive and semi‐automatic method for constructing or updating knowledge organization tools such as thesauri.

The paper proposes a comprehensive methodology for thesaurus construction and maintenance combining shallow NLP with a clustering algorithm and an information visualization interface. The resulting system TermWatch, extracts terms from a text collection, mines semantic relations between them using complementary linguistic approaches and clusters terms using these semantic relations. The clusters are mapped onto a 2D using an integrated visualization tool.

The clusters formed exhibit the different relations necessary to populate a thesaurus or ontology: synonymy, generic/specific and relatedness. The clusters represent, for a given term, its closest neighbours in terms of semantic relations.

This could change the way in which information professionals (librarians and documentalists) undertake knowledge organization tasks. TermWatch can be useful either as a starting point for grasping the conceptual organization of knowledge in a huge text collection without having to read the texts, then actually serving as a suggestive tool for populating different hierarchies of a thesaurus or an ontology because its clusters are based on semantic relations.

This lies in several points: combined use of linguistic relations with an adapted clustering algorithm, which is scalable and can handle sparse data. The paper proposes a comprehensive approach to semantic relations acquisition whereas existing studies often use one or two approaches. The domain knowledge maps produced by the system represents an added advantage over existing approaches to automatic thesaurus construction in that clusters are formed using semantic relations between domain terms. Thus while offering a meaningful synthesis of the information contained in the original corpus through clustering, the results can be used for knowledge organization tasks (thesaurus building and ontology population) The system also constitutes a platform for performing several knowledge‐oriented tasks like science and technology watch, textmining, query refinement.

The purpose of this paper is to explore the prevalence of potential problem gambling among Finnish prisoners; the associations between problem gambling and demographics, substance use and crime-related factors; and problem gamblers’ support preferences.

Prisoners (n=96) from two Finnish prisons were recruited between December 2017 and January 2018. The estimated response rate was 31 percent. Gambling problems were measured using the Brief Biosocial Gambling Screen. The participants were asked to report their gambling both for one year prior to their incarceration and for the past year. The independent variables were demographics (age, gender and marital status), substance use (alcohol, smoking and narcotics) and crime-related factors (crime type, prison type and previous sentence). Statistical significance (p) was determined using Fischer’s exact test.

Past-year pre-conviction problem gambling prevalence was 16.3 percent and past-year prevalence 15 percent. Age, gender, smoking, alcohol or illicit drug use were not associated with past-year problem gambling before sentencing. One-third of the prisoners (33.3 percent) who were sentenced for a property crime, financial crime or robbery were problem gamblers. One-quarter (24 percent) of all participants showed an interest in receiving support by identifying one or more support preferences. The most preferred type of support was group support in its all forms.

It is recommended that correctional institutions undertake systematic screening for potential problem gambling, and implement tailored intervention programs for inmates with gambling problems.

This study provides a deeper understanding of problem gambling in prisons. Problem gambling is associated with crime and also seems to be linked with serving a previous sentence. Early detection and tailored interventions for problem gambling may help to reduce reoffending rates.

First, the paper examines the extent of environmental disclosures (EDs) made by the Malaysian local governments (LGs) on their websites. Second, it examines the patterns of disclosure with the aim of investigating the underlying motive behind the EDs.

A descriptive content analysis was employed to measure the EDs. A disclosure instrument, based on the extant studies, is designed to facilitate the measurement process.

The results suggest that LGs of various levels, have taken the initiative to report EDs on their websites even in the absence of any statutory reporting regulations either by the state or federal government. Differences in the types of EDs are also apparent and consistent with the premise of legitimacy theory.

A paucity of prior studies measuring EDs made at the LG level is observed in the literature. Research evidence focussing on the EDs reported on the websites is even more scarce. This lack of research contributes to a limited understanding on the development and challenges of the environmental reporting practices within the developing country's perspective. Moreover, generalising the findings of studies conducted in developed countries may not be possible due to a number of regulatory, political and socio-economic factors.

The purpose of this paper is to contrast the speech genres in the original and the published versions of an article written by academic researchers and published in the US practitioner-oriented journal, Strategic Finance. The original version, submitted by the researchers, was rewritten by a professional editor in the USA before it was published.

The paper analyses the “persuasive” speech genre of the original version and the “authoritative” speech genre of the published version.

Although it was initially thought that the differences between the two versions were due to differences in the forms communication used by academics and practitioners, as the analysis progressed it became clear that the differences the authors were observing could be traced to more profound differences in philosophical assumptions about the “way of understanding and constructing a world”.

The choice of language and argumentation should be given careful attention when the authors craft the accounting frameworks and research papers, and especially when the authors seek to communicate the findings of the research to practitioners. However, the authors have focused on just one instance in which a text written by academics was re-written for publication in a practitioner journal.

The paper contrasts the rationalism of the persuasive speech genre and the pragmatism of the authoritative speech genre. It cautions academic researchers against uncritically adopting specific speech genres, whether they are academic or practitioner speech genres, without carefully reflecting on their relevance and implications for understanding the nature of the phenomenon being discussed.

The purpose of this paper is to quantify the characteristics of dual-harm behaviour in prison in comparison with sole self-harm or assault behaviour in prison, with an analysis of the distinguishing features.

Official data on in-prison incidents, demographic and offending information were analysed for 326 prisoners in two prisons in England.

Proportions of up to 42 per cent of offenders who assault others in prison will also engage in self-harm and vice versa. Dual-harm prisoners will engage in a broader and greater frequency of prison incidents than either sole group; with dual-harm prisoners reflecting greater proportions of damage to property and fire-setting. There were no differences in their time in prison or presence of serious violent current conviction, however, an index offence of drug supply was less likely in the dual-harm group, with minor violence slightly more likely in longer sentence prisoners. There was no difference for the dual-harm prisoners whether the first incident was self-harm or violence, with mean duration from sole to dual harm of less than three months.

In-prison behaviour can assist in the identification of prisoners at dual risk of harm. Greater inclusion of in-prison behaviour and awareness of dual harm in research methodologies may assist in improving risk management. A wider use of joint risk assessment and single case management approach is suggested for prisoners with dual-harm profile.

This is the first study on dual-harm behaviour in UK prisons and to evaluate their wider prison behaviour and offending characteristics.

The purpose of this study is to develop a review rating prediction method based on a supervised text mining approach for unrated customer reviews.

Using 2,851 hotel comment card (HCC) reviews, this paper manually labeled positive and negative comments with seven aspects (dining, cleanliness, service, entertainment, price, public, room) that emerged from the content of said reviews. After text preprocessing (tokenization, eliminating punctuation, stemming, etc.), two classifier models were created for predicting the reviews’ sentiments and aspects. Thus, an aggregate rating scale was generated using these two classifier models to determine overall rating values.

A new algorithm, Comment Rate (CRate), based on supervised learning, is proposed. The results are compared with another review-rating algorithm called location based social matrix factorization (LBSMF) to check the consistency of the proposed algorithm. It is seen that the proposed algorithm can predict the sentiments better than LBSMF. The performance evaluation is performed on a real data set, and the results indicate that the CRate algorithm truly predicts the overall rating with ratio 80.27%. In addition, the CRate algorithm can generate an overall rating prediction scale for hotel management to automatically analyze customer reviews and understand the sentiment thereof.

The review data were only collected from a resort hotel during a limited period. Therefore, this paper cannot explore the effect of independent variables on the dependent variable in context of larger period.

This paper provides a novel overall rating prediction technique allowing hotel management to improve their operations. With this feature, hotel management can evaluate guest feedback through HCCs more effectively and quickly. In this way, the hotel management will be able to identify those service areas that need to be developed faster and more effectively. In addition, this review rating prediction approach can be applied to customer reviews posted via online platforms for detecting review and rating reliability.

Manually analyzing textual information is time-consuming and can lead to measurement errors. Therefore, the primary contribution of this study is that although comment cards do not have rating values, the proposed CRate algorithm can predict the overall rating and understand the sentiment of the reviews in question.