Publication

Back to overview

A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Batanović Vuk, Cvetanović Miloš, Nikolić Boško,
Project Regional Linguistic Data Initiative
Show all

Original article (peer-reviewed)

Journal PLOS ONE
Volume (Issue) 15(11)
Page(s) e0242050 - e0242050
Title of proceedings PLOS ONE
DOI 10.1371/journal.pone.0242050

Open Access

URL http://doi.org/10.1371/journal.pone.0242050
Type of Open Access Publisher (Gold Open Access)

Abstract

Choosing a comprehensive and cost-effective way of articulating and annotating the sentiment of a text is not a trivial task, particularly when dealing with short texts, in which sentiment can be expressed through a wide variety of linguistic and rhetorical phenomena. This problem is especially conspicuous in resource-limited settings and languages, where design options are restricted either in terms of manpower and financial means required to produce appropriate sentiment analysis resources, or in terms of available language tools, or both. In this paper, we present a versatile approach to addressing this issue, based on multiple interpretations of sentiment labels that encode information regarding the polarity, subjectivity, and ambiguity of a text, as well as the presence of sarcasm or a mixture of sentiments. We demonstrate its use on Serbian, a resource-limited language, via the creation of a main sentiment analysis dataset focused on movie comments, and two smaller datasets belonging to the movie and book domains. In addition to measuring the quality of the annotation process, we propose a novel metric to validate its cost-effectiveness. Finally, the practicality of our approach is further validated by training, evaluating, and determining the optimal configurations of several different kinds of machine-learning models on a range of sentiment classification tasks using the produced dataset.
-