Improving Human Needs Categorization of Events with Semantic Classification

Abstract

Human Needs categories have been used to characterize the reason why an affective event is positive or negative. For example, "I got the flu" and "I got fired" are both negative (undesirable) events, but getting the flu is a Health problem while getting fired is a Financial problem. Previous work created learning models to assign events to Human Needs categories based on their words and contexts. In this paper, we introduce an intermediate step that assigns words to relevant semantic concepts. We create lightly supervised models that learn to label words with respect to 10 semantic concepts associated with Human Needs categories, and incorporate these labels as features for event categorization. Our results show that recognizing relevant semantic concepts improves both the recall and precision of Human Needs categorization for events.

Natural Language Processing for Policymaking

“…Information to Extract. Based on the large textual corpora, NLP models can be used to extract information that are useful for political decision-making, ranging from information about people, such as sentiment (Thelwall et al, 2011;Rosenthal et al, 2015), stance (Thomas et al, 2006;Gottipati et al, 2013;Stefanov et al, 2020;Luo et al, 2020), ideology (Hirst et al, 2010;Iyyer et al, 2014;Preoţiuc-Pietro et al, 2017), and reasoning on certain topics (Egami et al, 2018;Demszky et al, 2019;Camp et al, 2021), to factual information, such as main topics (Gottipati et al, 2013), events (Trappl, 2006;Mitamura et al, 2017;Ding and Riloff, 2018;Ding et al, 2019), andneeds (Sarol et al, 2020;Crayton et al, 2020;Paul and Frank, 2019) expressed in the data. The extracted information cannot only be about people, but also about political entities, such as the left-right political scales of parties and political actors (Slapin and Proksch, 2008;Glavaš et al, 2017b), which claims are raised by which politicians , and the legislative body's vote breakdown for state bills by backgrounds such as gender, rural-urban and ideological splits Davoodi et al (2020).…”

Learning to Classify Events from Human Needs Category Descriptions

“…Some of the previous work (Goyal et al, 2013;Deng and Wiebe, 2014;Ding and Riloff, 2016;Reed et al, 2017;Ding and Riloff, 2018b) aim to recognize the affective polarity of events. Recently, there have been many research work focusing on studying human needs and motives (Paul and Frank, 2019;Rashkin et al, 2018;Ding and Riloff, 2018a;Ding et al, 2019;Otani and Hovy, 2019) to achieve a deeper understanding of sentiment and emotion. However, all these work focused on building classifiers using manually labeled data, or using manual mapping rules from existing lexicons such as LIWC (Pennebaker et al, 2007), which requires a significant amount of manual effort.…”