Prediction of user opinion for products: A bag-of-words and collaborative filtering based approach

The rapid proliferation of social network services (SNS) gives people the opportunity to express their thoughts, opinions, and tastes on a wide variety of subjects such as movies or commercial items. Most item shopping websites currently provide SNS systems to collect users’ opinions, including rating and text reviews. In this context, user modeling and hyper-personalization of contents reduce information overload and improve both the efficiency of the marketing process and the user’s overall satisfaction. As is well known, users’ behavior is usually subject to sparsity and their preferences remain hidden in a latent subspace. A majority of recommendation systems focus on ranking the items by describing this subspace appropriately but neglect to properly justify why they should be recommended based on the user’s opinion. In this paper, we intend to extract the intrinsic opinion subspace from users’ text reviews –by means of collaborative filtering techniques– in order to capture their tastes and predict their future opinions on items not yet reviewed. We will show how users’ reviews can be predicted by using a set of words related to their opinions.2016/UEM16No data (2017)UE

Prediction of Opinion Keywords and Their Sentiment Strength Score Using Latent Space Learning Methods

Most item-shopping websites give people the opportunity to express their thoughts and opinions on items available for purchasing. This information often includes both ratings and text reviews expressing somehow their tastes and can be used to predict their future opinions on items not yet reviewed. Whereas most recommendation systems have focused exclusively on ranking the items based on rating predictions or user-modeling approaches, we propose an adapted recommendation system based on the prediction of opinion keywords assigned to different item characteristics and their sentiment strength scores. This proposal makes use of natural language processing (NLP) tools for analyzing the text reviews and is based on the assumption that there exist common user tastes which can be represented by latent review topics models. This approach has two main advantages: is able to predict interpretable textual keywords and its associated sentiment (positive/negative) which will help to elaborate a more precise recommendation and justify it, and allows the use of different dictionary sizes to balance performance and user opinion interpretability. To prove the feasibility of the adapted recommendation system, we have tested the capabilities of our method to predict the sentiment strength score of item characteristics not previously reviewed. The experimental results have been performed with real datasets and the obtained F1 score ranges from 66% to 77% depending on the dataset used. Moreover, the results show that the method can generalize well and can be applied to combined domain independent datasets.UEM E-modelo "E-Modelo Extracción de modelos de usuario y predicción de opinión a partir de textosSpanish Ministry of Economy and Competitiveness (MTM2014-57158-R)2.679 JCR (2020) Q2, 38/91 Engineering, Multidisciplinary0.435 SJR (2020) Q2, 366/2196 Computer Science ApplicationsNo data IDR 2019UE

UE POWER DECK. Baraja de gamificación del Plan de Ciencias

La baraja de gamificación Basic Science Power Deck es un elemento de dinamización de las clases que busca generar interacción profesor-estudiante. Cuando enfrentado a una pregunta cualquiera lanzada por el profesor durante una clase, el estudiante, generalmente, se encuentra en una situación que percibe como desventajosa. Contestar correctamente no tiene repercusión (más allá de la satisfacción personal), pero equivocarse frente a compañeros y profesor le supone subrayar públicamente su falta.SIN FINANCIACIÓNNo data 2016UE