Predicting Strategic Behavior from Free Text
Table 1: Attribute values for the texts of Section 4.2.
['Attribute', 'Text #1', 'Text #2', 'Text #3', 'Text #4', 'Text #5 [head to column names]CSV-Files/attribute-table.csv']
Having collected the data, we turned to extract commonsensical personality attributes from the texts. In service of that, two graduate students read the provided texts and extracted attributes that were considered appropriate as descriptors of the writers. In total, they highlighted 24 attributes, including adventurous, competitive, cynical, introvert, kind, passionate, pessimistic and rational, among others Then, we turned to scoring each text with a number in {0,1,…5} for each of these attributes, corresponding to the degree to which the text is affiliated with that attribute. We used Figure Eight (previously known as Crowd Flower) taking into account the necessary differences between human judgments when it comes to personality questions. Finally, the vector representation of each text was set to the vector whose coordinates correspond to the attributes, and the value in each entry is the average of the scores collected for this text-attribute pair. The author of the first text, for example, is most associated with attributes such as adventurous, friendly, kind, lightmineded, optimistic, passionate, and sincere. Naturally, the other authors that did not write about journeys, are not judged to be as adventurous as the author of the first text. Yet, the author of the fourth text, that discusses their personal journey in the career path, is also assigned a relatively high adventurous score.