The dramatic rise in the use of social network platforms such as Facebook or Twitter has resulted in the availability of vast and growing user-contributed repositories of data. Exploiting this data by extracting useful information from it has become a great challenge in data mining and knowledge discovery. A recently popular way of extracting useful information from social network platforms is to build indicators, often in the form of a time series, of general public mood by means of sentiment analysis. Such indicators have been shown to correlate with a diverse variety of phenomena. In this article we follow this line of work and set out to assess, in a rigorous manner, whether a public sentiment indicator extracted from daily Twitter messages can indeed improve the forecasting of social, economic, or commercial indicators. To this end we have collected and processed a large amount of Twitter posts from March 2011 to the present date for two very different domains: stock market and movie box office revenue . For each of these domains, we build and evaluate forecasting models for several target time series both using and ignoring the Twitter-related data. If Twitter does help, then this should be reflected in the fact that the predictions of models that use Twitter-related data are better than the models that do not use this data. By systematically varying the models that we use and their parameters, together with other tuning factors such as lag or the way in which we build our Twitter sentiment index, we obtain a large dataset that allows us to test our hypothesis under different experimental conditions. Using a novel decision-tree-based technique that we call summary tree we are able to mine this large dataset and obtain automatically those configurations that lead to an improvement in the prediction power of our forecasting models. As a general result, we have seen that nonlinear models do take advantage of Twitter data when forecasting trends in volatility indices, while linear ones fail systematically when forecasting any kind of financial time series. In the case of predicting box office revenue trend, it is support vector machines that make best use of Twitter data. In addition, we conduct statistical tests to determine the relation between our Twitter time series and the different target time series.

All right sreserved. We present GeoSRS, a hybrid recommender system for a popular location-based social network (LBSN), in which users are able to write short reviews on the places of interest they visit. Using state-of-the-art text mining techniques, our system recommends locations to users using as source the whole set of text reviews in addition to their geographical location. To evaluate our system, we have collected our own data sets by crawling the social network Foursquare. To do this efficiently, we propose the use of a parallel version of the Quadtree technique, which may be applicable to crawling/exploring other spatially distributed sources. Finally, we study the performance of GeoSRS on our collected data set and conclude that by combining sentiment analysis and text modeling, GeoSRS generates more accurate recommendations. The performance of the system improves as more reviews are available, which further motivates the use of large-scale crawling techniques such as the Quadtree.Preprin

The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model whose hidden layer is a regeneration process with Poisson immigration, Po-INAR(1), together with a mechanism that allows the estimation of the under-reporting in non-stationary count time series. A novelty of the model is that the expectation of the unobserved process’s innovations is a time-dependent function defined in such a way that information about the spread of an epidemic, as modelled through a Susceptible-Infectious-Removed dynamical system, is incorporated into the model. In addition, the parameter controlling the intensity of the under-reporting is also made to vary with time to adjust to possible seasonality or trend in the data. Maximum likelihood methods are used to estimate the parameters of the model.

This chapter describes the basic mechanics for building a forecasting model that uses as input sentiment indicators derived from textual data. In addition, as we focus our target of predictions on financial time series, we present a set of stylized empirical facts describing the statistical properties of lexicon-based sentiment indicators extracted from news on financial markets. Examples of these modeling methods and statistical hypothesis tests are provided on real data. The general goal is to provide guidelines for financial practitioners for the proper construction and interpretation of their own time-dependent numerical information representing public perception toward companies, stocks’ prices, and financial markets in general.