Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

Leveraging analytics to produce compelling and profitable film content

Producing compelling film content profitably is a top priority to the long-term prosperity of the film industry. Advances in digital technologies, increasing availabilities of granular big data, rapid diffusion of analytic techniques, and intensified competition from user generated content and original content produced by Subscription Video on Demand (SVOD) platforms have created unparalleled needs and opportunities for film producers to leverage analytics in content production. Built upon the theories of value creation and film production, this article proposes a conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics. The article further synthesizes the state-of-the-art research on and applications of these analytics, discuss the prospect of leveraging analytics in film production, and suggest fruitful avenues for future research with important managerial implications

Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation

On cross-domain social semantic learning

Approximately 2.4 billion people are now connected to the Internet, generating massive amounts of data through laptops, mobile phones, sensors and other electronic devices or gadgets. Not surprisingly then, ninety percent of the world's digital data was created in the last two years. This massive explosion of data provides tremendous opportunity to study, model and improve conceptual and physical systems from which the data is produced. It also permits scientists to test pre-existing hypotheses in various fields with large scale experimental evidence. Thus, developing computational algorithms that automatically explores this data is the holy grail of the current generation of computer scientists. Making sense of this data algorithmically can be a complex process, specifically due to two reasons. Firstly, the data is generated by different devices, capturing different aspects of information and resides in different web resources/ platforms on the Internet. Therefore, even if two pieces of data bear singular conceptual similarity, their generation, format and domain of existence on the web can make them seem considerably dissimilar. Secondly, since humans are social creatures, the data often possesses inherent but murky correlations, primarily caused by the causal nature of direct or indirect social interactions. This drastically alters what algorithms must now achieve, necessitating intelligent comprehension of the underlying social nature and semantic contexts within the disparate domain data and a quantifiable way of transferring knowledge gained from one domain to another. Finally, the data is often encountered as a stream and not as static pages on the Internet. Therefore, we must learn, and re-learn as the stream propagates. The main objective of this dissertation is to develop learning algorithms that can identify specific patterns in one domain of data which can consequently augment predictive performance in another domain. The research explores existence of specific data domains which can function in synergy with another and more importantly, proposes models to quantify the synergetic information transfer among such domains. We include large-scale data from various domains in our study: social media data from Twitter, multimedia video data from YouTube, video search query data from Bing Videos, Natural Language search queries from the web, Internet resources in form of web logs (blogs) and spatio-temporal social trends from Twitter. Our work presents a series of solutions to address the key challenges in cross-domain learning, particularly in the field of social and semantic data. We propose the concept of bridging media from disparate sources by building a common latent topic space, which represents one of the first attempts toward answering sociological problems using cross-domain (social) media. This allows information transfer between social and non-social domains, fostering real-time socially relevant applications. We also engineer a concept network from the semantic web, called semNet, that can assist in identifying concept relations and modeling information granularity for robust natural language search. Further, by studying spatio-temporal patterns in this data, we can discover categorical concepts that stimulate collective attention within user groups.Includes bibliographical references (pages 210-214)

A Survey on Feature Extraction Techniques, Classification Methods and Applications of Sentiment Analysis

Abstract Rapid developments in the era of IoT technologies, coupled with the espousal of social media tools and applications, have promoted the use of data analytics as a means to gain significant insights from unstructured data. Sentiment analysis is an approach that identifies data polarity to classify a text as positive, neutral, or negative. Also referred to as opinion mining or subjective mining, sentiment analysis has applications that range from marketing and customer service to clinical medicine. The application of sentiment analysis in the epoch of big data has proved invaluable in classifying sentiment and, in general, determining opinions from the average person’s frame of mind Several sentiment analysis techniques have been developed over the years. In this regard, this article presents a brief survey on the sentiment analysis applications, as well as feature extraction and sentiment classification techniques. This article surveys various feature extractions techniques and concludes that each technique has its own pros and cons, and can be combined for better results. The survey on classification methods suggests that hybrid methods provide finer results than individual ones. The survey of applications surmises that sentiment analysis as applied to different sectors, helps expand business opportunities. Also, the paper presents a few open challenges in carrying out sentiment analysis

Recommender systems in industrial contexts

This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A state of the art, which deals with the main techniques used in automated recommendation system: the two most commonly used algorithmic methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization methods are detailed. The state of the art presents also purely content-based methods, hybridization techniques, and the classical performance metrics used to evaluate the recommender systems. This state of the art then gives an overview of several systems, both from academia and industry (Amazon, Google ...). - An analysis of the performances and implications of a recommendation system developed during this thesis: this system, Reperio, is a hybrid recommender engine using KNN methods. We study the performance of the KNN methods, including the impact of similarity functions used. Then we study the performance of the KNN method in critical uses cases in cold start situation. - A methodology for analyzing the performance of recommender systems in industrial context: this methodology assesses the added value of algorithmic strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201

3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information.As these sources, methods, and applications become more interdisciplinary, the 3rd International Conference on Advanced Research Methods and Analytics (CARMA) is an excellent forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Doménech I De Soria, J.; Vicente Cuervo, MR. (2020). 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/149510EDITORIA

“WARES”, a Web Analytics Recommender System

Il est difficile d'imaginer des entreprises modernes sans analyse, c'est une tendance dans les entreprises modernes, même les petites entreprises et les entrepreneurs individuels commencent à utiliser des outils d'analyse d'une manière ou d'une autre pour leur entreprise. Pas étonnant qu'il existe un grand nombre d'outils différents pour les différents domaines, ils varient dans le but de simples statistiques d'amis et de visites pour votre page Facebook à grands et sophistiqués dans le cas des systèmes conçus pour les grandes entreprises, ils pourraient être shareware ou payés. Parfois, vous devez passer une formation spéciale, être un spécialiste certifiés, ou même avoir un diplôme afin d'être en mesure d'utiliser l'outil d'analyse. D'autres outils offrent une interface d’utilisateur simple, avec des tableaux de bord, pour satisfaire leur compréhension d’information pour tous ceux qui les ont vus pour la première fois. Ce travail sera consacré aux outils d'analyse Web. Quoi qu'il en soit pour tous ceux qui pensent à utiliser l'analyse pour ses propres besoins se pose une question: "quel outil doit je utiliser, qui convient à mes besoins, et comment payer moins et obtenir un gain maximum". Dans ce travail je vais essayer de donner une réponse sur cette question en proposant le système de recommandation pour les outils analytiques web –WARES, qui aideront l'utilisateur avec cette tâche "simple". Le système WARES utilise l'approche hybride, mais surtout, utilise des techniques basées sur le contenu pour faire des suggestions. Le système utilise certains ratings initiaux faites par utilisateur, comme entrée, pour résoudre le problème du “démarrage à froid”, offrant la meilleure solution possible en fonction des besoins des utilisateurs. Le besoin de consultations coûteuses avec des experts ou de passer beaucoup d'heures sur Internet, en essayant de trouver le bon outil. Le système lui–même devrait effectuer une recherche en ligne en utilisant certaines données préalablement mises en cache dans la base de données hors ligne, représentée comme une ontologie d'outils analytiques web existants extraits lors de la recherche en ligne précédente.It is hard to imagine modern business without analytics; it is a trend in modern business, even small companies and individual entrepreneurs start using analytics tools, in one way or another, for their business. Not surprising that there exist many different tools for different domains, they vary in purpose from simple friends and visits statistic for your Facebook page, to big and sophisticated systems designed for the big corporations, they could be free or paid. Sometimes you need to pass special training, be a certified specialist, or even have a degree to be able to use analytics tool, other tools offers simple user interface with dashboards for easy understanding and availability for everyone who saw them for the first time. Anyway, for everyone who is thinking about using analytics for his/her own needs stands a question: “what tool should I use, which one suits my needs and how to pay less and get maximum gain”. In this work, I will try to give an answer to this question by proposing a recommender tool, which will help the user with this “simple task”. This paper is devoted to the creation of WARES, as reduction from Web Analytics REcommender System. Proposed recommender system uses hybrid approach, but mostly, utilize content–based techniques for making suggestions, while using some user’s ratings as an input for “cold start” search. System produces recommendations depending on user’s needs, also allowing quick adjustments in selection without need of expensive consultations with experts or spending lots of hours for Internet search, trying to find out the right tool. The system itself should perform as an online search using some pre–cached data in offline database, represented as an ontology of existing web analytics tools, extracted during the previous online search