URI | http://purl.tuc.gr/dl/dias/7700363F-3D13-4B12-B304-4BFD6FAA76AD | - |
Αναγνωριστικό | https://doi.org/10.26233/heallink.tuc.66542 | - |
Γλώσσα | en | - |
Μέγεθος | 80 pages | en |
Τίτλος | Affective analysis and modeling of spoken dialogue transcripts | en |
Δημιουργός | Palogiannidi Elisavet | en |
Δημιουργός | Παλογιαννιδη Ελισαβετ | el |
Συντελεστής [Επιβλέπων Καθηγητής] | Koutsakis Polychronis | en |
Συντελεστής [Επιβλέπων Καθηγητής] | Κουτσακης Πολυχρονης | el |
Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Potamianos Alexandros | en |
Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Ποταμιανος Αλεξανδρος | el |
Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Mania Aikaterini | en |
Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Μανια Αικατερινη | el |
Εκδότης | Πολυτεχνείο Κρήτης | el |
Εκδότης | Technical University of Crete | en |
Ακαδημαϊκή Μονάδα | Technical University of Crete::School of Electrical and Computer Engineering | en |
Ακαδημαϊκή Μονάδα | Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών | el |
Περίληψη | At this thesis we propose affective models for the emotional analysis of lexical units in
various granularity levels. We propose and evaluate the use of an affective-semantic model
to expand the affective lexica of German, Greek, English, Spanish and Portuguese. Moti-
vated by the assumption that semantic similarity implies affective similarity, we use word
level semantic similarity scores as semantic features to estimate their corresponding af-
fective scores. Various context-based semantic similarity metrics are investigated using
contextual features that include both words and character n-grams. The model produces
continuous affective ratings in three dimensions (valence, arousal and dominance) for all
five languages, achieving consistent performance. We achieve classification accuracy (va-
lence polarity task) between 85% and 91% for all five languages. For morphologically
rich languages the proposed use of character n-grams is shown to improve performance.
Moreover, we created the first Greek affective lexicon, translating the words of the English
affective lexicon ANEW and assigning them to native speakers for affective annotation. It
contains human ratings for the three continuous affective dimensions of valence, arousal
and dominance for 1034 words.
Motivated by recent advances in the area of Compositional Distributional Semantic
Models (CDSMs), we propose a compositional approach for estimating continuous affective
ratings for adjective-noun (AN) and noun-noun (NN) pairs. The ratings are computed
for the three basic dimensions of continuous affective spaces, namely, valence, arousal and
dominance. We propose that similarly to the semantic modification that underlies CDSMs,
affective modification may occur within the framework of affective spaces, especially when
the constituent words of the linguistic structures under investigation form modifier-head
pairs (e.g., AN and NN). The affective content of the entire structure is determined from
the interaction between the respective constituents, i.e., the affect conveyed by the head
is altered by the modifier. In addition, we investigate the fusion of the proposed model
with the semantic-affective model proposed in literature applied both at word- and phrase-
level. The automatically computed affective ratings were evaluated against human ratings
in terms of correlation. The most accurate estimates are achieved via fusion and absolute
performance improvement up to 5% and 4% is reported for NN and AN, respectively.
We also investigate text based models for the affective analysis of sentences that are
mainly based on affective features. We investigate various datasets including news head-
lines, movie subtitles, Twitter status updates and spoken dialogue transcriptions and the
best (state-of-the-art) perfor | en |
Τύπος | Μεταπτυχιακή Διατριβή | el |
Τύπος | Master Thesis | en |
Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
Ημερομηνία | 2016-10-03 | - |
Ημερομηνία Δημοσίευσης | 2016 | - |
Θεματική Κατηγορία | Επεξεργασία φυσικής γλώσσας | el |
Θεματική Κατηγορία | Natural language processing | el |
Θεματική Κατηγορία | Affective computing | en |
Θεματική Κατηγορία | Sentiment analysis | en |
Θεματική Κατηγορία | Machine learning | en |
Θεματική Κατηγορία | Μηχανική μάθηση | el |
Βιβλιογραφική Αναφορά | Elisavet Palogiannidi, "Affective analysis and modeling of spoken dialogue transcripts", Master Thesis, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2016 | en |