TY - JOUR
T1 - Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis
AU - Mencarini, Letizia
AU - Hernández Farías, Delia Irazú
AU - LAI, MIRKO
AU - Patti, Viviana
AU - Sulis, Emilio
AU - Vignoli, Daniele
PY - 2019
Y1 - 2019
N2 - BACKGROUND
Demographers are increasingly interested in connecting demographic behaviour and
trends with ‘soft’ measures, i.e., complementary information on attitudes, values,
feelings, and intentions.
OBJECTIVE
The aim of this paper is to demonstrate how computational linguistic techniques can be
used to explore opinions and semantic orientations related to parenthood.
METHODS
In this article we scrutinize about three million filtered Italian tweets from 2014. First,
we implement a methodological framework relying on Natural Language Processing
techniques for text analysis, which is used to extract sentiments. We then run a
supervised machine-learning experiment on the overall dataset, based on the annotated
set of tweets from the previous stage. Consequently, we infer to what extent social
media users report negative or positive affect on topics relevant to the fertility domain.
RESULTS
Parents express a generally positive attitude towards being and becoming parents, but
they are also fearful, surprised, and sad. They also have quite negative sentiments about
their children’s future, politics, fertility, and parental behaviour. By exploiting
geographical information from tweets we find a significant correlation between theprevalence of positive sentiments about parenthood and macro-regional indicators of
both life satisfaction and fertility level.
CONTRIBUTION
We show how tweets can be used to represent soft measures such as attitudes, values,
and feelings, and we establish how they relate to demographic features. Linguistic
analysis of social media data provides a middle ground between qualitative studies and
more standard quantitative approaches.
AB - BACKGROUND
Demographers are increasingly interested in connecting demographic behaviour and
trends with ‘soft’ measures, i.e., complementary information on attitudes, values,
feelings, and intentions.
OBJECTIVE
The aim of this paper is to demonstrate how computational linguistic techniques can be
used to explore opinions and semantic orientations related to parenthood.
METHODS
In this article we scrutinize about three million filtered Italian tweets from 2014. First,
we implement a methodological framework relying on Natural Language Processing
techniques for text analysis, which is used to extract sentiments. We then run a
supervised machine-learning experiment on the overall dataset, based on the annotated
set of tweets from the previous stage. Consequently, we infer to what extent social
media users report negative or positive affect on topics relevant to the fertility domain.
RESULTS
Parents express a generally positive attitude towards being and becoming parents, but
they are also fearful, surprised, and sad. They also have quite negative sentiments about
their children’s future, politics, fertility, and parental behaviour. By exploiting
geographical information from tweets we find a significant correlation between theprevalence of positive sentiments about parenthood and macro-regional indicators of
both life satisfaction and fertility level.
CONTRIBUTION
We show how tweets can be used to represent soft measures such as attitudes, values,
and feelings, and we establish how they relate to demographic features. Linguistic
analysis of social media data provides a middle ground between qualitative studies and
more standard quantitative approaches.
KW - Twitter
KW - parenthood
KW - sentiment analysis
KW - social network
KW - subjective well-being
KW - Twitter
KW - parenthood
KW - sentiment analysis
KW - social network
KW - subjective well-being
UR - https://iris.uniupo.it/handle/11579/196182
U2 - 10.4054/DemRes.2019.40.25
DO - 10.4054/DemRes.2019.40.25
M3 - Article
SN - 2363-7064
VL - 40
SP - 693
EP - 724
JO - DEMOGRAPHIC RESEARCH
JF - DEMOGRAPHIC RESEARCH
ER -