Name
Abstract | ||
|---|---|---|
Automated text analysis (ATA) has been a widely used tool for determining the sentiment of writing samples. However, it is unclear how ATA compares to human ratings of text when estimating affect. There are costs and benefits associated with each method, and comparing the two approaches will help determine which one provides the most useful and accurate results. This study uses 279 journal entries from individuals with chronic pain in order to estimate the positive and negative affect scores reported directly by participants. We use Lasso to select the features that are most predictive of affect. Our results indicate that the model combining human coders and ATA accounts for the most variance in self-reported positive affect scores, resulting in adjusted R-squared=0.36. For negative affect scores, we obtain a lower adjusted R-squared=0.30 with the combined model, however, ATA results in significantly higher adjusted R-squared=0.27 compared to the model using only human coders, R-squared=0.14. This suggests that utilizing human coders may be the most beneficial when the focus is on positive affect, but automated text analysis may be sufficient when studying negative affect. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2914586.2914634 | HT |
Keywords | Field | DocType |
automated text analysis, human coding, expressive writing, feature selection, Lasso, LIWC, sentiment analysis | Data mining,Text mining,Feature selection,Computer science,Sentiment analysis,Lasso (statistics),Journal entry,Cost–benefit analysis,Artificial intelligence,Affect (psychology),Multimedia,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.43 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kathryn Schaefer Ziemer | 1 | 4 | 0.84 |
| Gizem Korkmaz | 2 | 98 | 11.10 |