Emotions are among the most pervasive aspects of human experience. They have long been of interest to social and behavioural sciences. Recently, emotions have attracted the attention of researchers in computer science and particularly in computational linguistics. Computational approaches to emotion analysis have also focused on various emotion modalities, but there is less effort in the direction of automatic recognition of the emotion expressed. Although some past work has addressed detecting emotions, detecting why an emotion arises is ignored.
In this work, we explore the task of classifying texts automatically by the emotions
expressed, as well as detecting the reason why a particular emotion is felt. We believe there is still a large gap between the theoretical research on emotions in psychology and emotion studies in computational linguistics. In our research, we try to fill this gap by considering both theoretical and computational aspects of emotions. Starting with a general explanation of emotion and emotion causes from the psychological and cognitive perspective, we clarify the definition that we base our work on. We explain what is feasible in the scope of text and what is practically doable based on the current NLP techniques and tools.
This work is organized in two parts: first part on Emotion Expression and the second
part on Emotion Stimulus.
In emotion expression detection, we start with shallow methodologies, such as corpus-based and lexical-based, followed by deeper methods considering syntactic and semantic relations in text. First, we demonstrate the usefulness of external knowledge resources, such as polarity and emotion lexicons, in automatic emotion detection. Next, we provide a description of the more advanced features chosen for characterizing emotional content based on the syntactic structure of sentences, as well as the machine learning techniques adopted for emotion classification.
The main novelty of our learning methodology is that it breaks down a problem into
hierarchical steps. It starts from a simpler problem to solve, and uses what is learnt to
extend the solution to solve harder problems. Here, we are learning emotion of sentences with one emotion word and we are extending the solution to sentences with more than one emotion word.
Next, we frame the detection of causes of emotions as finding a stimulus frame element as defined for the emotion frame in FrameNet – a lexical database of English based on the theory of meaning called Frame Semantics, which was built by manually annotating examples of how words are used in actual texts. According to FrameNet, an emotion stimulus is the person, event, or state of affairs that evokes the emotional response in the Experiencer. We believe it is the closest definition to emotion cause in order to answer why the experiencer feels that emotion.
We create the first ever dataset annotated with both emotion stimulus and emotion class; it can be used for evaluation or training purposes. We applied sequential learning methods to the dataset. We explored syntactic and semantic features in addition to corpus-based features. We built a model which outperforms all our carefully-built baselines. To show the robustness of our model and to study the problem more thoroughly, we apply those models to another dataset (that we used for the first part as well) to go deeper than detecting the emotion expressed and also detect the stimulus span which explains why the emotion was felt.
Although we first address emotion expression and emotion stimulus independently, we
believe that an emotion stimulus and the emotion itself are not mutually independent. In the last part, we address the relation of emotion expression and emotion stimulus by building four cases: both emotion expression and emotion stimulus occur at the same time, none of them appear in the text, there is only emotion expression, or only the emotion stimulus exists while there is no explicit mention of the emotion expressed. We found the last case the most challenging, so we study it in more detail.
Finally, we showcase how a clinical psychology application can benefit from our research. We also conclude our work and explain the future directions of this research.
Note: see http://www.eecs.uottawa.ca/~diana/resources/emotion_stimulus_data/
for all the data built for this thesis and discussed in it.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/34268 |
| Date | January 2016 |
| Creators | Ghazi, Diman |
| Contributors | Inkpen, Diana, Szpakowicz, Stanislaw |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0022 seconds