Measuring Cultural Relativity of Emotional Valenceand Arousal using Semantic Clustering and Twitter
Table 1: Distribution of tweets within our corpus.
['[BOLD] Emotion', '[BOLD] Asia', '[BOLD] Europe', '[BOLD] NA', '[BOLD] All']
Typing emotion keywords into the Internet is increasingly becoming a significant technique for individual expression. There now exists a rich available source of information about emotions on the Internet, because so many people spend time expressing how they feel in blogs, forums, social networking websites and the like. We use data from the microblogging website Twitter to perform large-scale analysis of the language used in thousands of expressions of emotions within tweets. Each emotion keyword (see selection criteria below) is given a five-minute streaming window in turn for the duration of the period, ensuring an even temporal distribution of Tweets is collected. We use the tweet’s timezone as an indication of the corresponding user’s geographical location; seeing as it is very unlikely that a Twitter user would select a timezone other than that which they reside in, it is somewhat safe to assume that this reflects the cultural origin of each user. Subcorpus creation. To mitigate micro-temporal effects, if the number of documents for a particular emotion is significantly greater than 1000, we use a modulus function to extract 1000 documents equally spaced across the subcorpus — for example, if a particular emotion in a particular subcorpus has 6000 documents, we take one document every six documents. We also create six control subcorpora so to compare our region-specific results with a baseline. We use the same modulus function to extract 1000 equally spaced tweets, but without any timezone clause, selecting six random starting points. Other than these anomalous subcorpora, the circumplex does illustrate how different cultures significantly conceptualise emotions differently, in keywords of valence and arousal. Interestingly, there are certain emotions in certain regions that stick out of our analysis. One example is the emotion depression; Asians find this emotion much more negative than all other cultures and control groups. This could be due to cultural differences such as coping strategies ( Aldwin \BBA Another example concerns the emotions happy and calm; Europeans and Asians find these emotions much more positive than North Americans and all control groups. Another suggests that Asians find interest a very positive and aroused emotion, compared to North Americans who conceptualise the same emotions, relatively, as negative and disengaged.