An assessment of deep learning models and word embeddings for toxicity detection within online textual comments

Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. The results suggest that Long-Short Term Memory layers in combination with mimicked word embeddings are a good choice for this task

A Survey of Graph Neural Networks for Social Recommender Systems

Social recommender systems (SocialRS) simultaneously leverage user-to-item interactions as well as user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users' tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention. In particular, with the advance of Graph Neural Networks (GNN), many GNN-based SocialRS methods have been developed recently. Therefore, we conduct a comprehensive and systematic review of the literature on GNN-based SocialRS. In this survey, we first identify 80 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). Then, we comprehensively review them in terms of their inputs and architectures to propose a novel taxonomy: (1) input taxonomy includes 5 groups of input type notations and 7 groups of input representation notations; (2) architecture taxonomy includes 8 groups of GNN encoder, 2 groups of decoder, and 12 groups of loss function notations. We classify the GNN-based SocialRS methods into several categories as per the taxonomy and describe their details. Furthermore, we summarize the benchmark datasets and metrics widely used to evaluate the GNN-based SocialRS methods. Finally, we conclude this survey by presenting some future research directions.Comment: GitHub repository with the curated list of papers: https://github.com/claws-lab/awesome-GNN-social-recsy

Recommending Privacy Settings for Internet-of-Things

Privacy concerns have been identified as an important barrier to the growth of IoT. These concerns are exacerbated by the complexity of manually setting privacy preferences for numerous different IoT devices. Hence, there is a demand to solve the following, urgent research question: How can we help users simplify the task of managing privacy settings for IoT devices in a user-friendly manner so that they can make good privacy decisions? To solve this problem in the IoT domain, a more fundamental understanding of the logic behind IoT users’ privacy decisions in different IoT contexts is needed. We, therefore, conducted a series of studies to contextualize the IoT users’ decision-making characteristics and designed a set of privacy-setting interfaces to help them manage their privacy settings in various IoT contexts based on the deeper understanding of users’ privacy decision behaviors. In this dissertation, we first present three studies on recommending privacy settings for different IoT environments, namely general/public IoT, household IoT, and fitness IoT, respectively. We developed and utilized a “data-driven” approach in these three studies—We first use statistical analysis and machine learning techniques on the collected user data to gain the underlying insights of IoT users’ privacy decision behavior and then create a set of “smart” privacy defaults/profiles based on these insights. Finally, we design a set of interfaces to incorporate these privacy default/profiles. Users can apply these smart defaults/profiles by either a single click or by answering a few related questions. The biggest limitation of these three studies is that the proposed interfaces have not been tested, so we do not know what level of complexity (both in terms of the user interface and the in terms of the profiles) is most suitable. Thus, in the last study, we address this limitation by conducting a user study to evaluate the new interfaces of recommending privacy settings for household IoT users. The results show that our proposed user interfaces for setting household IoT privacy settings can improve users’ satisfaction. Our research can benefit IoT users, manufacturers, and researchers, privacy-setting interface designers and anyone who wants to adopt IoT devices by providing interfaces that put their most prominent concerns in the forefront and that make it easier to set settings that match their preferences

An ebd-enabled design knowledge acquisition framework

Having enough knowledge and keeping it up to date enables designers to execute the design assignment effectively and gives them a competitive advantage in the design profession. Knowledge elicitation or acquisition is a crucial component of system design, particularly for tasks requiring transdisciplinary or multidisciplinary cooperation. In system design, extracting domain-specific information is exceedingly tricky for designers. This thesis presents three works that attempt to bridge the gap between designers and domain expertise. First, a systematic literature review on data-driven demand elicitation is given using the Environment-based Design (EBD) approach. This review address two research objectives: (i) to investigate the present state of computer-aided requirement knowledge elicitation in the domains of engineering; (ii) to integrate EBD methodology into the conventional literature review framework by providing a well-structured research question generation methodology. The second study describes a data-driven interview transcript analysis strategy that employs EBD environment analysis, unsupervised machine learning, and a range of natural language processing (NLP) approaches to assist designers and qualitative researchers in extracting needs when domain expertise is lacking. The second research proposes a transfer-learning method-based qualitative text analysis framework that aids researchers in extracting valuable knowledge from interview data for healthcare promotion decision-making. The third work is an EBD-enabled design lexical knowledge acquisition framework that automatically constructs a semantic network -- RomNet from an extensive collection of abstracts from engineering publications. Applying RomNet can improve the design information retrieval quality and communication between each party involved in a design project. To conclude, this thesis integrates artificial intelligence techniques, such as Natural Language Processing (NLP) methods, Machine Learning techniques, and rule-based systems to build a knowledge acquisition framework that supports manual, semi-automatic, and automatic extraction of design knowledge from different types of the textual data source

A framework for leveraging properties of user reviews in recommendation

With the growing volume of information online, it is increasingly harder for users to identify useful information to support their choices when interacting with different items. Review-based recommendation systems, which leverage reviews posted by users on items to estimate the users’ preferences, have been shown to be a credible solution for addressing the problem of identifying their preferred items. However, the actual usefulness of these reviews impact the effectiveness of the resulting recommender systems, especially with the enormous volume of available reviews online. In particular, as argued by the widely cited users’ adoption of information framework, users exhibit distinct preferences for reviews depending on the properties of these reviews (e.g. length, sentiment) when making decisions. Therefore, we argue that not all reviews are equally useful for different users. We aim to effectively modelling the personalised usefulness of reviews through the use of reviews’ properties when developing review-based recommendation techniques. Note that, few studies in the literature investigated the effectiveness of leveraging the properties of reviews to develop effective review-based recommendation approaches. This thesis aims to address this research gap by proposing a review-based recommendation framework. Such a framework models the personalised usefulness of reviews according to various reviews’ properties, including the reviews’ age, length, sentiment, ratings, helpfulness as judged by the users and helpfulness as predicted by a review helpfulness classifier. In particular, the thesis addresses two main challenges: (i) the availability of the attributes of reviews and (ii) the users’ preferences estimation. The first challenge refers to the difficulty of extracting particular review properties from their corresponding attributes. For example, extraction of the age property relies on the availability of the timestamps of the corresponding reviews. We address the availability of the reviews’ attributes to extract their sentiment and helpfulness properties with classification techniques. The sentiment property of reviews is estimated through effective state-of-the-art sentiment classifiers. We first evaluate the estimated reviews’ sentiment in comparison to the users’ ratings in typical recommendation approaches. Then, we introduce a sentiment attention mechanism to encode the estimated reviews’ sentiment. Our experiments show that the sentiment property can effectively replace the users’ ratings when estimating the user preferences. Moreover, by leveraging the estimated sentiment property of reviews, our proposed review-based rating prediction model shows improved performance compared to state-of-the-art rating prediction models. Next, the extraction of the reviews’ helpfulness property leverages the reviews’ helpful votes (i.e. a type of feedback given by other reviewers providing information on whether the corresponding review is helpful to them). However, the number of helpful votes for each review are not commonly available. In particular, we propose a novel weakly supervised review helpfulness classification correction approach (i.e. the Negative Confidence-aware Weakly Supervised (NCWS) approach), which leverages the confidence in a given review being unhelpful with respect to its age. We experimentally show that NCWS-based classifiers significantly outperform existing review helpfulness classifiers on two public review datasets. Moreover, the estimated helpfulness of reviews by NCWS-based classifiers can significantly improve the performance of a review-based rating prediction model. Next, to address our second challenge pertaining to the users’ preferences estimation, we aim to estimate their preferences when using reviews exhibiting different properties to accurately predict their preferred items. In particular, we propose two novel ranking-based recommendation approaches (named RPRM and BanditProp), which models the users’ preferences using different review properties with different techniques. The RPRM model applies the attention mechanism to model the usefulness of reviews according to different review properties. Unlike RPRM, the BanditProp model leverages existing bandit algorithms and introduces a novel contextual bandit algorithm to tackle the users’ preference estimation of using specific reviews’ properties to identify useful reviews. Our experiments show that RPRM can outperform stateof-the-art review-based recommendation models, and BanditProp can significantly outperform RPRM on two publicly available review datasets. These results validate the effectiveness of leveraging the review properties when examining the usefulness of reviews to improve the performances of review-based recommendation techniques. Overall, we contribute an effective review-based recommendation framework that make accurate recommendations by leveraging the reviews’ associated properties. This framework includes models for extracting properties from reviews, and various techniques that are required to integrate the learned properties, which, in turn and according to our conducted experiments, provide good approximations of a given users’ item preferences. These contributions make progress in the development of review-based recommendation techniques and could inspire future directions of research in recommendation systems

Neural Approaches to Relational Aspect-Based Sentiment Analysis. Exploring generalizations across words and languages

Jebbara S. Neural Approaches to Relational Aspect-Based Sentiment Analysis. Exploring generalizations across words and languages. Bielefeld: Universität Bielefeld; 2020.Everyday, vast amounts of unstructured, textual data are shared online in digital form. Websites such as forums, social media sites, review sites, blogs, and comment sections offer platforms to express and discuss opinions and experiences. Understanding the opinions in these resources is valuable for e.g. businesses to support market research and customer service but also individuals, who can benefit from the experiences and expertise of others. In this thesis, we approach the topic of opinion extraction and classification with neural network models. We regard this area of sentiment analysis as a relation extraction problem in which the sentiment of some opinion holder towards a certain aspect of a product, theme, or event needs to be extracted. In accordance with this framework, our main contributions are the following: 1. We propose a full system addressing all subtasks of relational sentiment analysis. 2. We investigate how semantic web resources can be leveraged in a neural-network-based model for the extraction of opinion targets and the classification of sentiment labels. Specifically, we experiment with enhancing pretrained word embeddings using the lexical resource WordNet. Furthermore, we enrich a purely text-based model with SenticNet concepts and observe an improvement for sentiment classification. 3. We examine how opinion targets can be automatically identified in noisy texts. Customer reviews, for instance, are prone to contain misspelled words and are difficult to process due to their domain-specific language. We integrate information about the character structure of a word into a sequence labeling system using character-level word embeddings and show their positive impact on the system's performance. We reveal encoded character patterns of the learned embeddings and give a nuanced view of the obtained performance differences. 4. Opinion target extraction usually relies on supervised learning approaches. We address the lack of available annotated data for specific languages by proposing a zero-shot cross-lingual approach for the extraction of opinion target expressions. We leverage multilingual word embeddings that share a common vector space across various languages and incorporate these into a convolutional neural network architecture. Our experiments with 5 languages give promising results: We can successfully train a model on annotated data of a source language and perform accurate prediction on a target language without ever using any annotated samples in that target language

Modeling the Dynamics of Consumer Behavior from Massive Interaction Data

Recent technological innovations (e.g. e-commerce platforms, automated retail stores) have enabled dramatic changes in people's shopping experiences, as well as the accessibility to incredible volumes of consumer-product interaction data. As a result, machine learning (ML) systems can be widely developed to help people navigate relevant information and make decisions. Traditional ML systems have achieved great success on various well-defined problems such as speech recognition and facial recognition. Unlike these tasks where datasets and objectives are clearly benchmarked, modeling consumer behavior can be rather complicated; for example, consumer activities can be affected by real-time shopping contexts, collected interaction data can be noisy and biased, interests from multiple parties (both consumers and producers) can be involved in the predictive objectives.The primary goal of this dissertation is to address the obstacles in modeling consumer activities through computational approaches, but with careful considerations from economic and societal perspectives. Intellectually, such models help us to understand the forces that guide consumer behavior. Methodologically, I build algorithms capable of processing massive interaction datasets by connecting well-developed ML techniques and well-established economic theories. Practically, my work has applications ranging from recommender systems, e-commerce and business intelligence