Exploring Latent Semantic Factors to Find Useful Product Reviews

Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews

The family drug & alcohol court (FDAC) evaluation project

This report presents the findings from the evaluation of the first pilot Family Drug and Alcohol Court (FDAC) in Britain. FDAC is a new approach to care proceedings, in cases where parental substance misuse is a key element in the local authority decision to bring proceedings. It is being piloted at the Inner London Family Proceedings Court in Wells Street. Initially the pilot was to run for three years, to the end of December 2010, but is now to continue until March 2012. The work is co-funded by the Department for Education (formerly the Department for Children, Schools and Families), the Ministry of Justice, the Home Office, the Department of Health and the three pilot authorities (Camden, Islington and Westminster). The evaluation was conducted by a research team at Brunel University, with funding from the Nuffield Foundation and the Home Office. FDAC is a specialist court for a problem that is anything but special. Its potential to help break the inter-generational cycle of harm associated with parental substance misuse goes straight to the heart of public policy and professional practice. Parental substance misuse is a formidable social problem and a key factor in around a third of long-term cases in children’s services in some areas. It is a major risk factor for child maltreatment, family separation and offending in adults, and for poor educational performance and substance misuse by children and young people. The parents’ many difficulties create serious problems for their children and place major demands on health, welfare and criminal justice services. For these reasons, parental substance misuse is a cross-cutting government agenda. FDAC is distinctive because it is a court-based family intervention which aims to improve children’s outcomes by addressing the entrenched difficulties of their parents. It has been adapted to English law and practice from a model of family treatment drug courts that is used widely in the USA and is showing promising results with a higher number of cases where parents and children were able to remain together safely, and with swifter alternative placement decisions for children if parents were unable to address their substance misuse successfully. The catalysts for the FDAC pilot were the encouraging evidence from the USA and concerns about the response to parental substance misuse through ordinary care proceedings in England: poor coordination of adult and children’s services; late interventions to protect children; delays in reaching decisions in court; and soaring costs of proceedings, linked to the cost of expert evidence.The work is co-funded by the Department for Education (formerly the Department for Children, Schools and Families), the Ministry of Justice, the Home Office, the Department of Health and the three pilot authorities (Camden, Islington and Westminster).1 The evaluation was conducted by a research team at Brunel University, with funding from the Nuffield Foundation and the Home Office

Encouraging Privacy-Aware Smartphone App Installation: Finding out what the Technically-Adept Do

Smartphone apps can harvest very personal details from the phone with ease. This is a particular privacy concern. Unthinking installation of untrustworthy apps constitutes risky behaviour. This could be due to poor awareness or a lack of knowhow: knowledge of how to go about protecting privacy. It seems that Smartphone owners proceed with installation, ignoring any misgivings they might have, and thereby irretrievably sacrifice their privacy

Knowledge Transfer Needs and Methods

INE/AUTC 12.3

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Understanding Hidden Memories of Recurrent Neural Networks

Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2017