Using eye-tracking data to create a weighted dictionary for sentiment analysis: the eye dictionary

Extracting information from written texts is of paramount importance to many entities (e.g. businesses, public organizations, individuals), but the exponential growth of available data has made this task beyond any single human being or business. Sentiment analysis is a tool to automatically transform the information extracted into knowledge. One of the main challenges is to assess if a text is positive or negative, which can be tackled using a dictionary where each word has a positive or negative associated value and then combining single-words values to express an overall text sentiment. In order to use such lexicon-based approach, we need an existing dictionary or to build a new one. In this work we present a new dictionary for sentiment analysis developed using eye-tracking data to determine the relevance of words and we assess its performances against other existing dictionaries

Markov chain to analyze web usability of a university website using eye tracking data

Web usability is a crucial feature of a website, allowing users to easily find information in a short time. Eye tracking data registered during the execution of tasks allow to measure web usability in a more objective way compared to questionnaires. In this work, we evaluated the web usability of the website of the University of Cagliari through the analysis of eye tracking data with qualitative and quantitative methods. Performances of two groups of students (i.e., high school and university students) across 10 different tasks were compared in terms of time to completion, number of fixations and difficulty ratio. Transitions between different areas of interest (AOI) were analyzed in the two groups using Markov chain. For the majority of tasks, we did not observe significant differences in the performances of the two groups, suggesting that the information needed to complete the tasks could easily be retrieved by students with little previous experience in using the website. For a specific task, high school students showed a worse performance based on the number of fixations and a different Markov chain stationary distribution compared to university students. These results allowed to highlight elements of the pages that can be modified to improve web usability

Investigating Shared Genetic Bases between Psychiatric Disorders, Cardiometabolic and Sleep Traits Using K-Means Clustering and Local Genetic Correlation Analysis

Psychiatric disorders are among the top leading causes of the global health-related burden. Comorbidity with cardiometabolic and sleep disorders contribute substantially to this burden. While both genetic and environmental factors have been suggested to underlie these comorbidities, the specific molecular underpinnings are not well understood. In this study, we leveraged large datasets from genome-wide association studies (GWAS) on psychiatric disorders, cardiometabolic and sleeprelated traits. We computed genetic correlations between pairs of traits using cross-trait linkage disequilibrium (LD) score regression and identified clusters of genetically correlated traits using k-means clustering. We further investigated the identified associations using two-sample mendelian randomization (MR) and tested the local genetic correlation at the identified loci. In the 7-cluster optimal solution, we identified a cluster including insomnia and the psychiatric disorders major depressive disorder (MDD), post-traumatic stress disorder (PTSD), and attention-deficit/hyperactivity disorder (ADHD). MR analysis supported the existence of a bidirectional association between MDD and insomnia and the genetic variants driving this association were found to affect gene expression in different brain regions. Some of the identified loci were further supported by results of local genetic correlation analysis, with body mass index (BMI) and C-reactive protein (CRP) levels suggested to explain part of the observed effects. We discuss how the investigation of the genetic relationships between psychiatric disorders and comorbid conditions might help us to improve our understanding of their pathogenesis and develop improved treatment strategies

Chapter Using eye-tracking to evaluate the viewing behavior on tourist landscapes

Every tourist website employs images to attract potential tourists. In particular, destination tourism websites use environmental images, such as landscapes, to attract the attention of tourists and to address their purchase choice. Nowadays the effectiveness of these tools has been enhanced by the use of eye-tracking technology. That allows measuring the exact eye position during the visualization of images, texts, or other visual stimuli. Consequently, eye-tracking data can be processed to obtain quantitative measures of viewing behavior that can be analyzed for several purposes in many fields such as to cluster consumers, to improve the effectiveness of a website and for neuroscience studies. This work is aimed to use eye-tracking technology to investigate user behavior according to different types of images (e.g. natural landscapes, city landscapes). Specifically, we compare different statistical descriptive tools with supervised and unsupervised models. Furthermore, we discuss the effectiveness of their results and their capacity to provide satisfactory and interpretable solutions that can be used by decision-makers

A scientometric analysis of the effect of COVID-19 on the spread of research outputs

The spread of the COVID-19 pandemic in 2020 had a huge impact on the life course of all of us. This rapid spread has also caused an increase in the research production in topics related to different aspects of COVID-19. Italy has been one of the first countries to be massively involved in the outbreak of the disease. In this paper, we present an extensive scientometric analysis of the research production both at global (entire literature produced in the first 2 years after the beginning of the pandemic) and local level (COVID-19 literature produced by authors with an Italian affiliation). Our results showed that US and China are the most active countries in terms of number of publications and that the number of collaborations between institutions varies depending on geographical distance. Moreover, we identified the medical-biological as the field with the greatest growth in terms of literature production. As regards the analysis focused on Italy, we have shown that most of the collaborations follow a geographical pattern, both externally (with a preference for European countries) and internally (two clusters of institutions, north versus center-south). Furthermore, we explored the relationship between the number of citations and variables obtained from the data set (e.g. number of authors). Using multiple correspondence analysis and quantile regression we shed light on the role of journal topics and impact factor, the type of article, the field of study and how these elements affect citations

Eye Tracking and Sentiment Analysis to evaluate user behavior and opinion

This dissertation concerns the use and integration of two different techniques, eye tracking and sentiment analysis, to improve our ability to extract information about human behavior and opinion. These techniques can be applied in several different fields in which it is desirable to be able to model behavior patterns or to conduct opinion mining using automated methods. While for some applications it is possible to ask information directly (for instance through a survey or an interview), in several situations this could either be not feasible or involve a high risk that questions could be misinterpreted, the answers may be deceptive, or the subject might not even know the answer. Eye tracking and sentiment analysis allow to obtain knowledge from different types of raw data, i.e. gaze position coordinates during visualization of a stimulus (eye tracking) and texts (sentiment analysis). However, there are several challenges related to the way in which data are collected, processed and analyzed. The main problem this thesis aims to address is how we can improve our ability to obtain knowledge on human behavior and opinion using eye tracking and sentiment analysis, and how these two methods can be integrated to address this task. Besides illustrating different studies in which we applied these two techniques to study the behavior of different types of users, we describe a new method to improve performance of sentiment analysis by leveraging eye tracking data. First, we focus on eye tracking and show how this technique can be used to identify aspects of web pages or digital flyers that might benefit of improvement, in order to provide a better user experience. We also show how eye tracking data can be useful to accomplish image classification tasks. Next, we apply sentiment analysis to understand how sentiment towards Italy shifted during the first phases of the COVID-19 outbreak by analyzing a large data set of tweets. We compare different sentiment analysis tools, identify a common breakpoint corresponding to the shift of sentiment scores and show that this change can serve as an early predictor of the evolution of stock exchange values. Finally, based on the hypothesis that the eye tracking technology can provide a substantial contribution to identify words that are able to attract more attention, and are thus potentially more relevant, we present a new dictionary that allows to perform sentiment analysis leveraging eye tracking data. We apply the Eye dictionary to the classification of different types of texts, showing that this tool is able to achieve a good performance, even when compared with dictionaries implementing a much higher number of words