Emilio Sulis

Figurative messages and affect in Twitter

The use of irony and sarcasm has been proven to be a pervasive phenomenon in social media posing a challenge to sentiment analysis systems. Such devices, in fact, can influence and twist the polarity of an utterance in different ways. A new dataset of over 10,000 tweets including a high variety of figurative language types, manually annotated with sentiment scores, has been released in the context of the task 11 of SemEval-2015. In this paper, we propose an analysis of the tweets in the dataset to investigate the open research issue of how separated figurative linguistic phenomena irony and sarcasm are, with a special focus on the role of features related to the multi-faceted affective information expressed in such texts. We considered for our analysis tweets tagged with #irony and #sarcasm, and also the tag #not, which has not been studied in depth before. A distribution and correlation analysis over a set of features, including a wide variety of psycholinguistic and emotional features, suggests arguments for the separation between irony and sarcasm. The outcome is a novel set of sentiment, structural and psycholinguistic features evaluated in binary classification experiments. We report about classification experiments carried out on a previously used corpus for #irony vs #sarcasm. We outperform in terms of F-measure the state-of-the-art results on this dataset. Overall, our results confirm the difficulty of the task, but introduce new data-driven arguments for the separation between #irony and #sarcasm. Interestingly, #not emerges as a distinct phenomenon.

ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm

This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figurative Language in Twitter, at SemEval 2015. Our system used a regression model and additional external resources to assign polarity values. A distinctive feature of our approach is that we used not only wordsentiment lexicons providing polarity annotations, but also novel resources for dealing with emotions and psycholinguistic information. These are important aspects to tackle in figurative language such as irony and sarcasm, which were represented in the dataset. The system also exploited novel and standard structural features of tweets. Considering the different kinds of figurative language in the dataset our submission obtained good results in recognizing sentiment polarity in both ironic and sarcastic tweets.

Processing Affect in Social Media: A Comparison of Methods to Distinguish Emotions in Tweets

Emotion analysis in social media is challenging. While most studies focus on positive and negative sentiments, the differentiation between emotions is more difficult. We investigate the problem as a collection of binary classification tasks on the basis of four opposing emotion pairs provided by Plutchik. We processed the content of messages by three alternative methods: structural and lexical features, latent factors, and natural language processing. The final prediction is suggested by classifiers deriving from the state of the art in machine learning. Results are convincing in the possibility to distinguish the emotions pairs in social media.

A Simulation-driven Approach in Risk-aware Business Process Management: A Case Study in Healthcare.

Risk management in business process is a key factor of success for organization as risks are part of every business activity. Errors may bring to increased costs, loss of quality as well as time delays, which in healthcare can bring to serious damages. This paper proposes a methodological framework to investigate risks in organizations by adopting a Business Process Management perspective that includes modeling and simulation of business processes. We applied our methodology to processes in the Blood Bank department of a large hospital. Our results show that a simulation-driven approach is an effective way to intercept and estimate real risks and to provide a decision support to guide the of department’s managers.

The role of the network of matches on predicting success in table tennis

ABSTRACT The influence of training, posture, nutrition or psychological attitudes on an athlete’s career is well described in literature. An additional factor of success that is widely recognized as crucial is the network of matches that an athlete plays during a season. The hypothesis is that the quality of a player’s opponents affects her long-term ranking and performance. Even though the relevance of these factors is widely recognized as important, a quantitative characterization is missing. In this paper, we try to fill this gap combining network analysis and machine learning to estimate the contribution of the network of matches in predicting an athlete’s success. We consider all the official games played by the Italian table tennis players between 2011 and 2016. We observe that the matches network shows scale-free behavior, typical of several real-world systems, and that different structural properties are positively correlated with the athletes’ performance (Spearman , p-value ). Using these findings, we implement three different tasks, such as talent identification, performance and ranking prediction. Results shows consistently that machine learning approaches are able to predict players’ success and that the topological features play an effective role in increasing their predictive power.

An Agent-Based Model of a Business Process: The Use Case of a Hospital Emergency Department

An application of Artificial Intelligence is computational simulation which reproduces the behavior of a system, such as an organization. Simulations provide benefits into business process management, also by combining scenarios and what-if analysis. This study explores the adoption of agent-based modeling technique, in addition to traditional discrete event simulations. The focus is on a real case study of an hospital emergency department. Following the construction of a new hospital, managers are interested in simulating the actual flows in the new configuration before the moving. In our model, patients and operators are agents, acting due to simple behavioral rules in the environment. The different activities are placed on the map of the department, to provide immediate understanding of bottlenecks and queues. While first results were validated from managers, next steps include the comparison of resulting flows between the new and the old department. Logistics analysis includes the time for moving agents between different wards.

Developing corpora and tools for sentiment analysis: the experience of the University of Turin group

English. The paper describes the ongoing experience at the University of Turin in developing linguistic resources and tools for sentiment analysis of social media. We describe in particular the development of Senti-TUT, a human annotated corpus of Italian Tweets including labels for sentiment polarity and irony, which has been recently exploited within the SENTIment POLarity Classification shared task at Evalita 2014. Furthermore, we report about our ongoing work on the Felicittà web-based platform for estimating happiness in Italian cities, which provides visualization techniques to interactively explore the results of sentiment analysis performed over Italian geotagged Tweets. Italiano. L’articolo presenta l’esperienza fatta presso l’Università di Torino nello sviluppo di risorse linguistiche e strumenti per la sentiment analysis di social media. In particolare, viene descritto Senti-TUT, un corpus di Tweet in Italiano, che include annotazioni relative alla polarità del sentiment e alla presenza di ironia, utilizzato nell’ambito del task di SENTIment POLarity Classification di Evalita 2014. Inoltre viene presentato il lavoro su Felicittà, una piattaforma Web per la stima della felicità nelle città italiane, che fornisce diverse modalità di visualizzazione del grado di felicità che emerge da un’analisi del sentiment su messaggi Twitter geolocalizzati in