Foundations of statistical natural language processing
Foundations of statistical natural language processing
UA-ZBSA: a headline emotion classification through web information
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UPAR7: a knowledge-based system for headline sentiment tagging
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
How latent is latent semantic analysis?
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
| Hi-index | 0.00 |
Emotions are reflected both in verbal and written communication. If in the first case they can be easier to trace due to some specific features (body language, voice tone or inflections), in the second it can be quite tricky to grasp the underlying emotions carried by a written text. Therefore we propose a novel automatic method for analyzing emotions induced by texts, more specifically a reader's most likely emotional state after reading a news article. In other words, our goal is to determine how reading a piece of news affects a person's emotional state and to adjust these values based on his/her current state. From a more technical perspective, our system (Emo2 --- Emo tions Mo nitor) combines a context independent approach (actual evaluation of the news employing specific natural language processing techniques and Latent Semantic Analysis) with the influences of user's present emotional state estimated through his/her specific feedback for building a more accurate image of a person's emotional state.