Obrada negacije u kratkim neformalnim tekstovima u cilju poboljšanja klasifikacije sentimenta
Докторанд
Ljajić, Adela B.Ментор
Stojković, SuzanaЧланови комисије
Stanković, MilenaJanković, Dragan
Stoimenov, Leonid
Kajan, Ejub
Метаподаци
Приказ свих података о дисертацијиСажетак
In this dissertation, the method for classifying short informal
texts by sentiment was proposed. The improvement was achieved by
processing the rule of syntactic negation in the Serbian language. The
complexity of the grammar of the Serbian language imposes the need
to systematically approach the phenomena of negation and to use the
linguistic resources involved in the creation of rules for the negation
treatment in its processing. The resources used are negation signals,
negative quantifiers, negation intensifiers, and negation neutralizers.
In addition to language resources for the application of the rules of
negation, the general sentiment lexicon of positive and negative terms
was used in the classification by sentiment. The evaluation of the used
method was performed over a set of tweets in Serbian. Lexicon based
method, as well as the supervised method of machine learning, were
used for evaluation. The method presented in both cases is compared
with two baseline met...hods: the first one that does not process the
negation and the other that processes the negation, but without the
rules for processing a syntactic negation. In the case where a method
based on sentiment lexicon was used, the accuracy of the
classification is considerably higher in relation to the two baseline
methods, and the relative improvements of this method with respect
to the first baseline method are the following: for the entire dataset -
up to 10.62%, for a set of tweets containing negation - up to 26.63%
and for a set of tweets containing negations that were processed using
the rules - up to 31.16%. When using the machine learning method,
higher accuracy of the classification is obtained than in the case of the
lexicon-based method: for three classes - up to 69.76% and for two
classes - up to 91.15%. However, the method of machine learning
produces fewer improvements: for three classes up to 2.65% and for
two classes up to 1.65%. The results showed a statistically significant
improvement if the detected rules of negation are included in the short
informal text classification method by sentiment. The results showed
a statistically significant improvement if the detected rules of
negation are included in the short informal text classification method
by sentiment.
Факултет:
Универзитет у Нишу, Електронски факултетДатум одбране:
04-10-2019Пројекти:
- Развој уређаја за тренинг пилота и динамичку симулацију лета модерних борбених авиона и то 3-осне центрифуге и 4-осног уређаја за просторну дезоријентацију пилота (RS-35023)
- Нове информационе технологије за аналитичко одлучивање базиране на организацији експеримента и опсервацији и њихова примена у биолошким, економским и социолошким системима (RS-44007)
- Развој софтверских система за подршку пословања малих и средњих предузећа (RS-13012)