Login | Register

Automatic Detection of Emotions and Distress in Textual Data


Automatic Detection of Emotions and Distress in Textual Data

Mohammadi, Elham ORCID: https://orcid.org/0000-0001-7890-8180 (2019) Automatic Detection of Emotions and Distress in Textual Data. Masters thesis, Concordia University.

[thumbnail of Mohammadi_MCS_S2020.pdf]
Text (application/pdf)
Mohammadi_MCS_S2020.pdf - Accepted Version


Online data can be analyzed for many purposes, including the prediction of stock market, business, and political planning. Online data can also be used to develop systems for the automatic emotion detection and mental health assessment of users. These systems can be used as complementary measures in monitoring online forums by detecting users who are in need of attention.

In this thesis, we first present a new approach for contextual emotion detection, i.e. emotion detection in short conversations. The approach is based on a neural feature extractor, composed of a recurrent neural network with an attention mechanism, followed by a final classifier, that can be neural or SVM-based. The results from our experiments showed that, by providing a higher and more robust performance, SVM can act as a better final classifier in comparison to a feed-forward neural network.

We then extended our model for emotion detection, and created an ensemble approach for the task of distress detection from online data. This extended approach utilizes several attention-based neural sub-models to extract features and predict class probabilities, which are later used as input features to a Support Vector Machine (SVM) making the final classification. Our experiments show that using an ensemble approach which makes use different sub-models accessing diverse sources of information can improve classification in the absence of a large annotated dataset.

The extended model was evaluated on two shared tasks, CLPsych and eRisk 2019, which aim at suicide risk assessment, and early risk detection of anorexia, respectively. The model ranked first in tasks A and C of CLPsych 2019 (with macro-average F1 scores of 0.481 and 0.268, respectively), and ranked first in the first task of eRisk 2019 in terms of F1 and latency-weighted F1 scores (0.71 and 0.69, respectively).

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Mohammadi, Elham
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:10 October 2019
Thesis Supervisor(s):Kosseim, Leila
ID Code:986153
Deposited By: Elham Mohammadi
Deposited On:26 Jun 2020 13:26
Last Modified:26 Jun 2020 13:26
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top