| 1 |
Mental Illness and Suicide Ideation Detection Using Social Media Data
|
|
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Predicting Depression and Suicide Ideation in the Canadian Population Using Social Media Data
|
|
Skaik, Ruba. - : Université d'Ottawa / University of Ottawa, 2021
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Automatic Poetry Classification and Chronological Semantic Analysis
|
|
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Introduction to the special issue on Language in Social Media: Exploiting discourse and other contextual information
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03044246 ; Benamara, Farah; Taboada, Maïté; Inkpen, Diana; ACL: Association for Computational Linguistics. The MIT Press, 44 (4, special issue), pp.663-681, 2018, Computational Linguistics, ISSN: 0891-2017. ⟨10.1162/coli_a_00333⟩ ; https://www.mitpressjournals.org/doi/full/10.1162/coli_a_00333 (2018)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Automatic Poetry Classification Using Natural Language Processing
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Stance Detection and Analysis in Social Media
|
|
|
|
Abstract:
Computational approaches to opinion mining have mostly focused on polarity detection of product reviews by classifying the given text as positive, negative or neutral. While, there is less effort in the direction of socio-political opinion mining to determine favorability towards given targets of interest, particularly for social media data like news comments and tweets. In this research, we explore the task of automatically determining from the text whether the author of the text is in favor of, against, or neutral towards a proposition or target. The target may be a person, an organization, a government policy, a movement, a product, etc. Moreover, we are interested in detecting the reasons behind authors’ positions. This thesis is organized into three main parts: the first part on Twitter stance detection and interaction of stance and sentiment labels, the second part on detecting stance and the reasons behind it in online news comments, and the third part on multi-target stance classification. One may express favor (or disfavor) towards a target by using positive or negative language. Here, for the first time, we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets, as well as for sentiment. These targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. We develop a simple stance detection system that outperforms all 19 teams that participated in a recent shared task competition on the same dataset (SemEval-2016 Task #6). Additionally, access to both stance and sentiment annotations allows us to conduct several experiments to tease out their interactions. Next, we proposed a novel framework for joint learning of stance and reasons behind it. This framework relies on topic modeling. Unlike other machine learning approaches for argument tagging which often require a large set of labeled data, our approach is minimally supervised. The extracted arguments are subsequently employed for stance classification. Furthermore, we create and make available the first dataset of online news comments manually annotated for stance and arguments. Experiments on this dataset demonstrate the benefits of using topic modeling, particularly Non-Negative Matrix Factorization, for argument detection. Previous models for stance classification often treat each target independently, ignoring the potential (sometimes very strong) dependency that could exist among targets. However, in many applications, there exist natural dependencies among targets. In this research, we relieve such independence assumptions in order to jointly model the stance expressed towards multiple targets. We present a new dataset that we built for this task and make it publicly available. Next, we show that an attention-based encoder-decoder framework is very effective for this problem, outperforming several alternatives that jointly learn dependent subjectivity through cascading classification or multi-task learning.
|
|
Keyword:
Deep Learning; Natural Language Processing; Social Media Analysis; Stance Detection; Text Classification
|
|
URL: https://doi.org/10.20381/ruor-20460
http://hdl.handle.net/10393/36180
|
|
BASE
|
|
Hide details
|
|
|
| 11 |
Identifying Expressions of Emotions and Their Stimuli in Text
|
|
Ghazi, Diman. - : Université d'Ottawa / University of Ottawa, 2016
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Sentiment Analysis of Data from Online Forums on the Newborn Genome Sequencing
|
|
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Graphon: A Comparison of Grapheme-to-phoneme Conversion Performance between an Automated System and Primary Grade Students
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Approaches of anonymisation of an SMS corpus
|
|
|
|
In: 14th International Conference on Intelligent Text Processing and Computational Linguistics ; CICLing: Conference on Intelligent Text Processing and Computational Linguistics ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-00816285 ; CICLing: Conference on Intelligent Text Processing and Computational Linguistics, Mar 2013, Samos, Greece. pp.77-88, ⟨10.1007/978-3-642-37247-6_7⟩ ; http://www.cicling.org/2013/ (2013)
|
|
BASE
|
|
Show details
|
|
|
| 18 |
A Computational Approach to the Analysis and Generation of Emotion in Text
|
|
|
|
BASE
|
|
Show details
|
|
|
| 19 |
An Unsupervised Approach to Detecting and Correcting Errors in Text
|
|
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Towards the Development of an Automatic Diacritizer for the Persian Orthography based on the Xerox Finite State Transducer
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
