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Mental Illness and Suicide Ideation Detection Using Social Media Data
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Predicting Depression and Suicide Ideation in the Canadian Population Using Social Media Data
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Skaik, Ruba. - : Université d'Ottawa / University of Ottawa, 2021
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Automatic Poetry Classification and Chronological Semantic Analysis
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Introduction to the special issue on Language in Social Media: Exploiting discourse and other contextual information
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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)
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Automatic Poetry Classification Using Natural Language Processing
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Identifying Expressions of Emotions and Their Stimuli in Text
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Ghazi, Diman. - : Université d'Ottawa / University of Ottawa, 2016
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Sentiment Analysis of Data from Online Forums on the Newborn Genome Sequencing
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Graphon: A Comparison of Grapheme-to-phoneme Conversion Performance between an Automated System and Primary Grade Students
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Approaches of anonymisation of an SMS corpus
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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)
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A Computational Approach to the Analysis and Generation of Emotion in Text
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Abstract:
Sentiment analysis is a field of computational linguistics involving identification, extraction, and classification of opinions, sentiments, and emotions expressed in natural language. Sentiment classification algorithms aim to identify whether the author of a text has a positive or a negative opinion about a topic. One of the main indicators which help to detect the opinion are the words used in the texts. Needless to say, the sentiments expressed in the texts also depend on the syntactic structure and the discourse context. Supervised machine learning approaches to sentiment classification were shown to achieve good results. Classifying texts by emotions requires finer-grained analysis than sentiment classification. In this thesis, we explore the task of emotion and mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard flat classification approach. We also show that using sentiment orientation features improves the performance of classification. We used the LiveJournal blog corpus as a dataset to train and evaluate our method. Another contribution of this work is extracting paraphrases for emotion terms based on the six basics emotions proposed by Ekman (\textit{happiness, anger, sadness, disgust, surprise, fear}). Paraphrases are different ways to express the same information. Algorithms to extract and automatically identify paraphrases are of interest from both linguistic and practical points of view. Our paraphrase extraction method is based on a bootstrapping algorithms that starts with seed words. Unlike in previous work, our algorithm does not need a parallel corpus. In Natural Language Generation (NLG), paraphrasing is employed to create more varied and natural text. In our research, we extract paraphrases for emotions, with the goal of using them to automatically generate emotional texts (such as friendly or hostile texts) for conversations between intelligent agents and characters in educational games. Nowadays, online services are popular in many disciplines such as: e-learning, interactive games, educational games, stock market, chat rooms and so on. NLG methods can be used in order to generate more interesting and normal texts for such applications. Generating text with emotions is one of the contributions of our work. In the last part of this thesis, we give an overview of NLG from an applied system's points of view. We discuss when NLG techniques can be used; we explained the requirements analysis and specification of NLG systems. We also, describe the main NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Moreover, we describe our Authoring Tool that we developed in order to allow writers without programming skills to automatically generate texts for educational games. We develop an NLG system that can generate text with different emotions. To do this, we introduce our pattern-based model for generation. We show our model starts with initial patterns, then constructs extended patterns from which we choose ``final'' patterns that are suitable for generating emotion sentences. A user can generate sentences to express the desired emotions by using our patterns. Alternatively, the user can use our Authoring Tool to generate sentences with emotions. Our acquired paraphrases will be employed by the tool in order to generate more varied outputs.
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Keyword:
Authoring Tool; Bootstrapping; Emotion Analysis; Natural Language; Natural Language Generation; Paraphrase; Processing; Sentiment Orientation
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URL: https://doi.org/10.20381/ruor-4713
http://hdl.handle.net/10393/20137
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An Unsupervised Approach to Detecting and Correcting Errors in Text
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Towards the Development of an Automatic Diacritizer for the Persian Orthography based on the Xerox Finite State Transducer
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