Measuring the happiness of large-scale written expression: Songs, Blogs, and Presidents
arXiv:1703.09774 · doi:10.1007/s10902-009-9150-9
Abstract
The importance of quantifying the nature and intensity of emotional states at the level of populations is evident: we would like to know how, when, and why individuals feel as they do if we wish, for example, to better construct public policy, build more successful organizations, and, from a scientific perspective, more fully understand economic and social phenomena. Here, by incorporating direct human assessment of words, we quantify happiness levels on a continuous scale for a diverse set of large-scale texts: song titles and lyrics, weblogs, and State of the Union addresses. Our method is transparent, improvable, capable of rapidly processing Web-scale texts, and moves beyond approaches based on coarse categorization. Among a number of observations, we find that the happiness of song lyrics trends downward from the 1960's to the mid 1990's while remaining stable within genres, and that the happiness of blogs has steadily increased from 2005 to 2009, exhibiting a striking rise and fall with blogger age and distance from the equator.
arXiv comment13 pages, 11 figures, 3 tables
References in corpus (1)
Cited by in corpus (22)
- Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter
- The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place
- Comparing and Combining Sentiment Analysis Methods
- Anger is More Influential Than Joy: Sentiment Correlation in Weibo
- The Minor Fall, the Major Lift: Inferring Emotional Valence of Musical Chords through Lyrics
- The Evolution of Popular Music: USA 1960-2010
- Collective emotions online and their influence on community life
- Happiness and the Patterns of Life: A Study of Geolocated Tweets
- An Agent-Based Model of Collective Emotions in Online Communities
- Positive words carry less information than negative words
- Positivity of the English language
- Visitors to urban greenspace have higher sentiment and lower negativity on Twitter
- Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts
- A planetary nervous system for social mining and collective awareness
- Mining social media data for biomedical signals and health-related behavior
- FrameAxis: Characterizing Microframe Bias and Intensity with Word Embedding
- Quantitative Analysis of Bloggers Collective Behavior Powered by Emotions
- Temporal Limits of Privacy in Human Behavior
- Identifying long-term periodic cycles and memories of collective emotion in online social media
- Augmenting semantic lexicons using word embeddings and transfer learning
- Reply to Garcia et al.: Common mistakes in measuring frequency dependent word characteristics
- Quantifying Collective Emotions: Japan's Societal Trends Through Enhanced Sentiment Index Using POMS2 and SNS