“Buon appetito!” - Analyzing Happiness in Italian Tweets
Authors: Pierpaolo Basile, Nicole Novielli (Università di Bari Aldo Moro, 2018)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
We report the results of an exploratory study aimed at investigating the language of happiness in Italian tweets. Speciﬁcally, we conduct a time-wise analysis of the happiness load of tweets by leveraging a lexicon of happiness extracted from 8.6M tweets. Furthermore, we report the results of a statistical linguistic analysis aimed at extracting the most frequent concepts associated with the happy and sad words in our lexicon.

Introduction
The widespread diffusion of social media has reshaped the way we interact and communicate. Among others, microblogging platforms as Twitter are becoming extremely popular and people constantly use them for sharing opinions about facts of public interest. Furthermore, its worldwide adoption and the fact that tweets are publicly available, makes Twitter an extremely appealing virtual place for researchers interested in language analysis as a mean to investigate social phenomena (Bollen et al., 2009; Garimella et al., 2016). In addition, recent research showed how microblogging is also used for self-disclosure of individual feelings (Roberts et al., 2012; Andalibi et al., 2017). As such, microblogs constitute an invaluable wealth of data ready to be mined for discovering affective stereotypes (Joseph et al., 2017) using corpus-based approaches to linguistic ethnography (Mihalcea and Liu, 2006). Such analyses, can further enhance our understanding on how people conceptualize the experience of emotions and what are their more common triggers. Recent studies even envisaged the emergence of tools for monitoring the public mood 1 and health through the analysis of Twitter users’ reaction to major social, political, economics events (Bollen et al., 2009). In this study we report the results of an exploratory analysis of the language of happiness in Twitter. In particular, we perform a partial replication of the approach proposed by (Mihalcea and Liu, 2006) for mining sources of happiness in blog posts. The contributions of this paper are as follows. First, we extract a happiness dictionary from a sample of about 8.6M tweets from the TWITA corpus of Italian tweets (Basile and Nissim, 2013). For each word in the dictionary, we compute a happiness factor by adapting the approach proposed in the original study. Furthermore, we perform a qualitative investigation of the 100 happiest and saddest words by mapping them into psycholinguistic word categories (see Section 2). As a second step, we use our dictionary to perform a time-wise analysis of happiness as shared in different hours and days of the week (see Section 3). Third, we extract concepts most frequently associated with happy words in our dictionary, which we map into WordNet super-senses (see Section 4). We discuss limitations and provide suggestions for future work in Section 5.

Conclusion
We performed an exploratory analysis of the lexicon and concepts associated with happiness in Italian tweets. We leveraged a corpus of happy and sad tweets to extract a ”happiness dictionary’, which we use to perform a time-wise analysis of happiness on Twitter and to extract the most frequent concepts and psycholinguistic categories associated to positive and negative emotions. This study is a partial replication of the previous one by (Mihalcea and Liu, 2006) on blog posts. The main differences with respect to the original study are in the size, language and source of the corpus used for extracting the happiness lexicon. Speciﬁcally, (Mihalcea and Liu, 2006) rely on a collection of 10,000 blog posts in English from LiveJournal.com to extract a list of happy/sad words with their associated happiness scores, while we leverage a bigger corpus consisting of 8.6M Italian tweets. Furthermore, the blog posts were labeled as happy or sad by their authors. Conversely, for tweets we relied on silver labeling based on the presence of emoticons as a proxy the author self-reporting of her own positive or negative emotions. Our analysis of psycholinguistic categories and the extraction of concepts and WordNet supersenses associated with them reveals interesting ﬁndings. Happiness appears related to the social aspects of life while sad tweets mainly revolves around self-centered negative feelings and thoughts. In addition, our-time wise analysis reveals a mid-week drop in happiness also observed in the original study. We also observe that happiness is high in the morning and decreases over the day. As a future work, it would be interesting to investigate if time-wise analysis based on hours produces consistent results if a weekday or the weekend is considered and if emotion-triggering concepts associated with happiness also vary over time. We are aware of the main limitations of this study. First of all, by relying on microblogs we are probably able to mine emotion triggers that do not necessarily coincide with those shared in daily face-to-face conversations or reported in private logs. Furthermore, we do not attempt to make any categorization of the authors of tweets. Indeed, different target user groups could be studied to fulﬁll speciﬁc research goals and enable perspective applications, i.e. for supporting creative writing or for providing personalized recommendations based on moods. Finally, we consider only Twitter as a source of data. The same methodology could produce different results if applied to other social media. Indeed, recent research (Andalibi et al., 2017) showed that other media, such as Instagram, are also used for sharing extremely private emotions, such as feelings linked to depression. Based on these observations, further replications could focus on ﬁner-grained emotions, also leveraging corpora from different platforms and including consideration of demographics and geographical information (Mitchell et al., 2013; Allisio et al., 2013) as additional dimensions of analysis.