Volume 40 - Article 25 | Pages 693–724
Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis
By Letizia Mencarini, Delia Irazú Hernández Farías, Mirko Lai, Viviana Patti, Emilio Sulis, Daniele Vignoli
References
Aggarwal, C.C. and Abdelzaher, T.F. (2013). Social sensing. In: Aggarwal, C.C. (ed.). Managing and mining sensor data. New York: Springer: 237–297.
Allisio, L., Mussa, V., Bosco, C., Patti, V., and Ruffo, G. (2013). Felicittà: Visualizing and estimating happiness in Italian cities from geotagged Tweets. In: Battaglino, C., Bosco, C., Cambria, E., Damiano, R., Patti, V., and Rosso, P. (eds.). Proceedings of the 1st International Workshop on Emotion and Sentiment in Social and Expressive Media (ESSEM 2013). Turin: CEUR Workshop Proceedings: 95–106.
Attardi, G., Basile, V., Bosco, C., Caselli, T., Dell’Orletta, F., Montemagni, S., Patti, V., Simi, M., and Sprugnoli, R. (2015). State of the art language technologies for Italian: The EVALITA 2014 perspective. Intelligenza Artificiale 9(1): 43–61.
Baccianella, S., Esuli, A., and Sebastiani, F. (2010). SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Calzolari, N., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., and Tapias, D. (eds.). Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC 2010). Paris: ELRA.
Barbieri, F., Basile, V., Croce, D., Nissim, M., Novielli, N., and Patti, V. (2016). Overview of the EVALITA 2016 SENTIment POLarity classification task. In: Basile, P., Cutugno, F., Nissim, M., Patti, V., and Sprugnoli, R. (eds.). Proceedings of the 5th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA 2016). Turin: Accademia University Press.
Basile, V., Bolioli, A., Nissim, M., Patti, V., and Rosso, P. (2014). Overview of the Evalita 2014 SENTIment POLarity classification task. In: Bosco, C., Cosi, P., Dell’Orletta, F., Falcone, M., Montemagni, S., and Simi, M. (eds.). Proceedings of the 4th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA 2014). Pisa: Pisa University Press: 50–57.
Basile, V., Cutugno, F., Nissim, M., Patti, V., and Sprugnoli, R. (2016). Overview of the 5th evaluation campaign of Natural Language Processing and Speech Tools for Italian. In: Basile, P., Cutugno, F., Nissim, M., Patti, V., and Sprugnoli, R. (eds.). Proceedings of the 5th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA 2016). Turin: Accademia University Press.
Basile, V. and Nissim, M. (2013). Sentiment analysis on Italian tweets. In: Balahur, A., van der Goot, E., and Montoyo, A. (eds.). Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Atlanta: ACL: 100–107.
Billari, F.C., Cavalli, N., Qian, E., and Weber, I. (2017). Footprints of family change: A study based on Twitter. Paper presented at the Annual Meeting of the Population Association of America, Chicago, USA, April 27–29, 2017.
Billari, F.C., D’Amuri, F., and Marcucci, J. (2013). Forecasting births using Google. Paper presented at the New Orleans, USA.
Blumenstock, J.E., Gillick, D., and Eagle, N. (2010). Who’s calling? Demographics of mobile phone use in Rwanda. In: Eagle, N. and Horvitz, E. (eds.). AAAI Spring Symposium: Artificial Intelligence for Development 2010. Menlo Park: AAAI Press: 116–117.
Bollen, J. and Mao, M. (2011). Twitter mood as a stock market predictor. Computer 44(10): 91–94.
Bosco, C., Allisio, L., Mussa, V., Patti, V., Ruffo, G., Sanguinetti, M., and Sulis, E. (2014). Detecting happiness in Italian tweets: Towards an evaluation dataset for sentiment analysis in Felicittà. In: Schuller, B., Buitelaar, P., Devillers, L., Pelachaud, C., Declerck, T., Batliner, A., Rooso, P., and Gaines, S. (eds.). Proceedings of the 5th International Workshop on Emotion, Social Signals, Sentiment and Linked Open Data. Paris: ELRA: 56–63.
Bosco, C., Patti, V., and Bolioli, A. (2013). Developing corpora for sentiment analysis: The case of irony and senti-TUT. IEEE Intelligent Systems 28(2): 55–63.
Bosco, C., Patti, V., and Bolioli, A. (2015). Developing corpora for sentiment analysis: The case of irony and senti-TUT. In: Yang, Q. and Wooldridge, M. (eds.). Proceedings of the 24th International Conference on Artificial Intelligence. Menlo Park: AAAI Press: 4158–4162.
Buscaldi, D. and Hernández-Farías, D.I. (2015). Sentiment analysis on microblogs for natural disasters management: A study on the 2014 Genoa floodings. In: Gangemi, A., Leonardi, S., and Panconesi, A. (eds.). Proceedings of the 24th International Conference on World Wide Web Companion (WWW 2015). New York: ACM: 1185–1188.
Castells, M. (2000). The rise of the network society. Cambridge: Blackwell.
Ceron, A., Curini, L., and Iacus, S.M. (2014). Social media e sentiment analysis: L’evoluzione dei fenomeni sociali attraverso la Rete. Milan: Springer.
Cetre, S., Clark, A.E., and Senik, C. (2016). Happy people have children: Choice and self-selection into parenthood. European Journal of Population 32(3): 445–473.
Clark, R., Ogawa, N., Lee, S.-H., and Matsukura, R. (2008). Older workers and national productivity in Japan. Population and Development Review 34(Supplement): 257–274.
De Choudhury, M., Counts, S., and Horvitz, E. (2013). Predicting postpartum changes in emotion and behavior via social media. In: Mackay, W.E., Brewster, S., and Bødker, S. (eds.). Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2013). New York: ACM: 3267–3276.
Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F.R., Gaughan, A.E., Blondel, V.D., and Tatem, A.J. (2014). Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences 111(45): 15888–15893.
Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a national index. American Psychologist 55(1): 34–43.
Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Barnden, J., and Reyes, A. (2015). Semeval-2015 task 11: Sentiment analysis of figurative language in Twitter. In: Nakov, P., Zesch, T., Cer, D., and Jurgens, D. (eds.). Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). Denver: ACL: 470–478.
Han, J., Pei, J., and Kamber, M. (2011). Data mining: Concepts and techniques. Woltham: Elsevier.
Hansen, T. (2012). Parenthood and happiness: A review of folk theories versus empirical evidence. Social Indicators Research 108(1): 29–64.
Hernández-Farías, D.I., Buscaldi, D., and Priego-Sánchez, B. (2014). IRADABE: Adapting English lexicons to the Italian sentiment polarity classification task. In: Basili, R., Lenci, A., and Magnini, B. (eds.). Proceedings of the 1st Italian Conference on Computational Linguistics (CLiC-IT 2017). Pisa: Pisa University Press: 75–81.
Hilbert, M. and López, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science 332(6025): 60–65.
Hitsch, G.J., Hortaçsu, A., and Ariely, D. (2010). Matching and sorting in online dating. American Economic Review 100(1): 130–163.
Hu, M. and Liu, B. (2004). Mining and summarizing customer reviews. In: Kim, W., Kohavi, R., Gehrke, J., and DuMouchel, W. (eds.). Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004). New York: ACM: 168–177.
King, G. (2011). Ensuring the data-rich future of the social sciences. Science 331(6018): 719–721.
Kohler, H.P., Behrman, J.R., and Skytthe, A. (2005). Partner + children = Happiness? The effects of partnerships and fertility on well‐being. Population and Development Review 31(3): 407–445.
Kohler, H.P. and Mencarini, L. (2016). The parenthood happiness puzzle: An introduction to special issue. European Journal of Population 32(3): 327–338.
Lai, M., Virone, D., Bosco, C., and Patti, V. (2015). Debate on political reforms in Twitter: A hashtag-driven analysis of political polarization. In: Proceedings of 2015 IEEE International Conference on Data Science and Advanced Analytics, Special Track on Emotion and Sentiment in Intelligent Systems and Big Social Data Analysis. Paris: IEEE: 1–9.
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., and Alstyne, M.V. (2009). Social science: Computational social science. Science 323(5915): 721–723.
Lesthaeghe, R. (2010). The unfolding story of the second demographic transition. Population and Development Review 36(2): 211–251.
Liu, B. (2010). Sentiment analysis and subjectivity. Boca Raton: Taylor and Francis.
Margolis, R. and Myrskylä, M. (2011). A global perspective on happiness and fertility. Population and Development Review 37(1): 29–56.
Maynard, D. and Greenwood, M. (2014). Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In: Calzolari, N., Choukri, K., Declerck, T., Loftsson, H., Maegaard, B., Mariani, J., Moreno, A., Odijk, J., and Piperidis, S. (eds.). Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014). Paris: ELRA.
McDonald, P. (2013). Societal foundations for explaining fertility: Gender equity. Demographic Research 28(34): 981–994.
Mencarini, L. (2018). The potential of the computational linguistic analysis of social media for population studies. In: Nissim, M., Patti, V., Plank, B., and Wagner, C. (eds.). Proceedings of the 2nd Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media. New Orleans: ACL: 62–68.
Meo, R. and Sulis, E. (2017). Processing affect in social media: A comparison of methods to distinguish emotions in tweets. ACM Transactions on Internet Technology 17(1): 7.
Mitchell, L., Frank, M.R., Harris, K.D., Dodds, P.S., and Danforth, C.M. (2013). The geography of happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PLoS ONE 8(5): e64417.
Mohammad, S.M., Kiritchenko, S., Parinaz, S., Xiaodan, Z., and Cherry, C. (2016). Semeval-2016 task 6: Detecting stance in tweets. In: Bethard, S., Carpuat, M., Cer, D., Jurgens, D., Nakov, P., and Zesch, T. (eds.). Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016). San Diego: ACL: 31–41.
Mohammad, S.M., Zhu, X., Kiritchenko, S., and Martin, J. (2015). Sentiment, emotion, purpose, and style in electoral tweets. Information Processing and Management 51(4): 480–499.
Myrskylä, M. and Margolis, R. (2014). Happiness: Before and after the kids. Demography 51(5): 1843–1866.
Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., and Stoyanov, V. (2016). SemEval-2016 task 4: Sentiment analysis in Twitter. In: Bethard, S., Carpuat, M., Cer, D., Jurgens, D., Nakov, P., and Zesch, T. (eds.). Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016). San Diego: ACL: 1–18.
Nielsen, F.A. (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. In: Rowe, M., Stankovic, M., Dadzie, A.-S., and Hardey, M. (eds.). Proceedings of the ESWC 2011 Workshop on ‘Making Sense of Microposts’: Big things come in small packages (MSM 2011). Heraklion: CEUR Workshop Proceedings: 93–98.
Nissim, M. and Patti, V. (2016). Semantic aspects in sentiment analysis. In: Pozzi, F.A., Fersini, E., Messina, E., and Liu, B. (eds.). Sentiment analysis in social networks. Cambridge: Elsevier: 31–48.
Plutchik, R. (2011). The nature of emotions. American Scientist 89(4): 344–350.
Quercia, D., Crowcroft, J., Ellis, J., and Capra, L. (2012). Tracking ‘gross community happiness’ from tweets. In: Gergle, D., Ringel Morris, M., Bjørn, P., and Konstan, J. (eds.). Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (CSCM 2012). New York: ACM: 965–968.
Rangel, F., Rosso, P., Chugur, I., Potthast, M., Trenkmann, M., Stein, B., Verhoeven, B., and Daelemans, W. (2014). Overview of the 2nd author profiling task at PAN 2014. In: Cappellato, L., Ferro, N., Halvey, M., and Kraaij, W. (eds.). Working notes for the CLEF 2014 Conference. Sheffield: CEUR Workshop Proceedings: 898–927.
Reimsbach-Kounatze, C. (2015). The proliferation of ‘big data’ and implications for official statistics and statistical agencies: A preliminary analysis. Paris: OECD (OECD Digital Economy Papers 245).
Reis, B.Y. and Brownstein, J.S. (2010). Measuring the impact of health policies using internet search patterns: The case of abortion. BMC Public Health 10(514): 1–5.
Reyes, A. and Rosso, P. (2014). On the difficulty of automatically detecting irony: Beyond a simple case of negation. Knowledge and Information Systems 40(3): 595–614.
Senior, J. (2015). All joy and no fun: The paradox of modern parenthood. New York: Little Brown Book Group.
Sobolevsky, S., Szell, M., Campari, R., Couronné, T., Smoreda, Z., and Ratti, C. (2013). Delineating geographical regions with networks of human interactions in an extensive set of countries. PLoS ONE 8(12): e81707.
Stranisci, M., Bosco, C., Patti, V., and Hernández-Farias, D.I. (2016). Annotating sentiment and irony in the online Italian political debate on #labuonascuola. In: Calzolari, N., Choukri, K., Declerck, T., Goggi, S., Grobelnik, M., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., and Piperidis, S. (eds.). Proceedings of the 10th edition of the Language Resources and Evaluation Conference (LREC 2016). Paris: ELRA.
Sulis, E., Hernández-Farías, D.I., Rosso, P., Patti, V., and Ruffo, G. (2016). Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems 108: 132–143.
Sulis, E., Lai, M., Vinai, M., and Sanguinetti, M. (2015). Exploring sentiment in social media and official statistics: A general framework. In: Bosco, C., Cambria, E., Damiano, R., Patti, V., and Rosso, P. (eds.). Proceedings of the 2nd International Conference on Emotion and Sentiment in Social and Expressive Media (ESSEM 2015). New York: ACM: 96–105.
Tumasjan, A., Sprenger, T.O., Sandner, P.G., and Welpe, I.M. (2011). Predicting elections with Twitter: What 140 characters reveal about political sentiment. In: Nicolov, N., Shanahan, J.G., Adamic, L., Baeza-Yates, R., and Counts, S. (eds.). Proceedings of the 5th International Conference on Weblogs and Social Media. Menlo Park: AAAI Press: 178–185.
Van de Kaa, D.J. (1987). Europe’s second demographic transition. Population Bulletin 42(1): 1–59.
Verma, S., Vieweg, S., Corvey, W., Palen, L., Martin, J.H., Palmer, M., Schram, A., and Anderson, K.M. (2011). Natural language processing to the rescue? Extracting ‘situational awareness’ tweets during mass emergency. In: Nicolov, N., Shanahan, J.G., Adamic, L., Baeza-Yates, R., and Counts, S. (eds.). Proceedings of the 5th International Conference on Weblogs and Social Media. Menlo Park: AAAI Press: 385–392.
Whissell, C. (2009). Using the revised dictionary of affect in language to quantify the emotional undertones of samples of natural languages. Psychological Reports 2(105): 509–521.
Wilson, D. (2006). The pragmatics of verbal irony: Echo or pretence? Lingua 116(10): 1722–1743.
Zagheni, E., Garimella, V.R.K., and Weber, I. (2014). Inferring international and internal migration patterns from twitter data. In: Chung, C.-W., Broder, A., Shim, K., and Suel, T. (eds.). Proceedings of the 23rd International Conference on World Wide Web. New York: ACM: 439–444.
Zagheni, E. and Weber, I. (2015). Demographic research with non-representative internet data. International Journal of Manpower 36(1): 13–25.
Zagheni, E. and Weber, I. (2012). You are where you E-mail: Using E-mail data to estimate international migration rates. In: Contractor, N., Uzzi, B., Macy, M., and Nejdl, W. (eds.). Proceedings of the 4th Annual ACM Web Science Conference. New York: ACM: 348–351.
Zagheni, E., Weber, I., and Gummadi, K. (2017). Estimate stock of migrants using Facebook’s advertising platform. Population and Development Review online first.