In a scientific study of dream content, artificial intelligence has been utilized to automatically score dream content. An initial attempt focused on scoring for emotional tone of dream reports. The contribution of this thesis demonstrates methods by which accuracy of such a system can be improved beyond text-mining. It was hypothesized that data extraction based on psychological processes will provide significant information that would produce an accurate model. In our first article, the significance of words expressed in dream reports, along with their associated words was explored. Extraction and inclusion of these associations provided detailed information that improved automatic scoring of positive and negative affect even though these associations exhibited skewed distribution. The second article demonstrated how normalization of the data was possible and how it could result in a more accurate model. Our last article was able to demonstrate that the model can differentiate between male and female dreams.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/20290 |
| Date | January 2011 |
| Creators | Amini, Reza |
| Contributors | De Koninck, Joseph |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0019 seconds