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The use of sentiment analysis methods has increased in recent years across a wide range of disciplines. Despite the potential impact of the development of opinions during political elections, few studies have focused on the analysis of sentiment dynamics and their characterization from statistical and mathematical perspectives. In this paper, we apply a set of basic methods to analyze the statistical and temporal dynamics of sentiment analysis on political campaigns and assess their scope and limitations. To this end, we gathered thousands of Twitter messages mentioning political parties and their leaders posted several weeks before and after the 2019 Spanish presidential election. We then followed a twofold analysis strategy: (1) statistical characterization using indices derived from well-known temporal and information metrics and methods –including entropy, mutual information, and the Compounded Aggregated Positivity Index– allowing the estimation of changes in the density function of sentiment data; and (2) feature extraction from nonlinear intrinsic patterns in terms of manifold learning using autoencoders and stochastic embeddings. The results show that both the indices and the manifold features provide an informative characterization of the sentiment dynamics throughout the election period. We found measurable variations in sentiment behavior and polarity across the political parties and their leaders and observed different dynamics depending on the parties’ positions on the political spectrum, their presence at the regional or national levels, and their nationalist or globalist aspirations.

Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at the same time an interesting way to identify singularities among peers. We conclude that temporal properties of messages provide with information about the sentiment dynamics, which is different in terms of lexicons and users, but commonalities can be exploited in this field using appropriate temporal digital processing tools.

With the emergence of information and communication technologies, a large amount of data has turned available for the organizations, which creates expectations on their value and content for management purposes. However, the exploratory analysis of available organizational data based on emerging Big Data technologies are still developing in terms of operative tools for solid and interpretable data description. In this work, we addressed the exploratory analysis of organization databases at early stages where little quantitative information is available about their efficiency. Categorical and metric single-variable tests are proposed and formalized in order to provide a mass criterion to identify regions in forms with clusters of significant variables. Bootstrap resampling techniques are used to provide nonparametric criteria in order to establish easy-to-use statistical tests, so that single-variable tests are represented each on a visual and quantitative statistical plot, whereas all the variables in a given form are jointly visualized in the so-called chromosome plots. More detailed profile plots offer deep comparison knowledge for categorical variables across the organization physical and functional structures, while histogram plots for numerical variables incorporate the statistical significance of the variables under study for preselected Pareto groups. Performance grouping is addressed by identifying two or three groups according to some representative empirical distribution of some convenient grouping feature. The method is applied to perform a Big-Data exploratory analysis on the follow-up forms of Spanish Red Cross, based on the number of interventions and on a by-record basis. Results showed that a simple one-variable blind-knowledge exploratory Big-Data analysis, as the one developed in this paper, offers unbiased comparative graphical and numerical information that characterize organizational dynamics in terms of applied resources, available capacities, and productivity. In particular, the graphical and numerical outputs of the present analysis proved to be a valid tool to isolate the underlying overloaded or under-performing resources in complex organizations. As a consequence, the proposed method allows a systematic and principled way for efficiency analysis in complex organizations, which combined with organizational internal knowledge could leverage and validate efficient decision-making.

Machine learning techniques, more commonly known today as artificial intelligence, are playing an increasingly important role in all aspects of our lives [...]

The search for an unbiased company valuation method to reduce uncertainty, whether or not it is automatic, has been a relevant topic in social sciences and business development for decades. Many methods have been described in the literature, but consensus has not been reached. In the companion paper we aimed to review the assessment capabilities of traditional company valuation model, based on company’s intrinsic value using the Discounted Cash Flow (DCF). In this paper, we capitalized on the potential of exogenous information combined with Machine Learning (ML) techniques. To do so, we performed an extensive analysis to evaluate the predictive capabilities with up to 18 different ML techniques. Endogenous variables (features) related to value creation (DCF) were proved to be crucial elements for the models, while the incorporation of exogenous, industry/country specific ones, incrementally improves the ML performance. Bagging Trees, Supported Vector Machine Regression, Gaussian Process Regression methods consistently provided the best results. We concluded that an unbiased model can be created based on endogenous and exogenous information to build a reference framework, to price and benchmark Enterprise Value for valuation and credit risk assessment.