A Corpus-based Approach to Finding Happiness

Description:

This paper discusses how to locate emotions.

Creator(s):
Creation Date: March 2006
Partner(s):
UNT College of Engineering
Collection(s):
UNT Scholarly Works
Usage:
Total Uses: 82
Past 30 days: 10
Yesterday: 2
Creator (Author):
Liu, Hugo

Massachusetts Institute of Technology

Creator (Author):
Mihalcea, Rada, 1974-

University of North Texas

Date(s):
  • Creation: March 2006
Description:

This paper discusses how to locate emotions.

Degree:
Note:

Copyright 2006 American Association for Artificial Intelligence (AAAI). All rights reserved. http://www.aaai.org

Note:

Abstract: What are the sources of happiness and sadness in everyday life? In this paper, the authors employ 'linguistic ethnography' to seek out where happiness lies in our everyday lives by considering a corpus of blogposts from the LiveJournal community annotated with happy and sad moods. By analyzing this corpus, the authors derive lists of happy and sad words and phrases annotated by their 'happiness factor'. Various semantic analyses performed with this wordlist reveal the happiness trajectory of a 24-day (3am and 9-10p are most happy), and a 7-day week (Wednesdays are saddest), and compare the socialness and human-centeredness of happy descriptions versus sad descriptions. The authors evaluate our corpus-based approach in a classification task and contrast our wordlist with emotionally-annotated wordlists produced by experimental focus groups. Having located happiness temporally and semantically within this corpus of everyday life, the paper concludes by offering a corpus-inspired livable recipe for happiness.

Physical Description:

6 p.

Language(s):
Subject(s):
Keyword(s): linguistic ethnography | semantic analysis | moods | emotions
Source: American Association for Artificial Intelligence (AAAI) Spring Symposium on Computational Approaches to Weblogs, 2006, Standford, California, United States
Contributor(s):
Partner:
UNT College of Engineering
Collection:
UNT Scholarly Works
Identifier:
  • ARK: ark:/67531/metadc30980
Resource Type: Paper
Format: Text
Rights:
Access: Public