CruzAffect at AffCon 2019 Shared Task: A feature-rich approach to characterize happiness
Table 1: The distribution of the age groups, and the probability of P(agency=yes) and P(social=yes) for each age group. The overall probability of P(agency=yes) is 0.74 and P(social=yes) is 0.53. The middle-age group is less likely to identify their happy moment with agency but more likely to identify the moment with social, while kids are more likely to identify their happy moment with both agency and social.
['[BOLD] age', '[BOLD] frequency', '[BOLD] P(agency=yes)', '[BOLD] P(social=yes)']
Profile Features: The corpus include demographic features collected via a survey: age, country, gender, married, parenthood, reflection, and duration. To reduce sparsity, we convert the country feature into language feature, assuming that the people who speak the same language might share similar culture, and thus similar happy moments. After this conversion, we have 48 different languages from the 70 countries, the largest group is English (80.3%), then Hindi (15.9%), corresponding to 79.8% examples from USA and 15.87% examples from IND. We also bin age into groups, assuming that different age groups would have different general happy moments. There are 70 features after the feature preprocessing. We aim to test whether the features extracted from text are sufficient for affective sentiment analysis, and whether the profile features improve performance.