=Paper=
{{Paper
|id=Vol-3346/Short2
|storemode=property
|title=A Review of Ontology-Based Approaches for Sentiment Analysis: Possible Improvements on
the
Brazilian Affective Computing Scenario
|pdfUrl=https://ceur-ws.org/Vol-3346/Short2.pdf
|volume=Vol-3346
|authors=Erica Carneiro,Gustavo Guedes,Kele Belloze
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==A Review of Ontology-Based Approaches for Sentiment Analysis: Possible Improvements on
the
Brazilian Affective Computing Scenario==
https://ceur-ws.org/Vol-3346/Short2.pdf
A Review of Ontology-Based Approaches for
Sentiment Analysis: Possible Improvements on the
Brazilian Affective Computing Scenario
Erica Carneiro1 , Gustavo Guedes1 and Kele Belloze1
1
Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ), 229 Maracana Av, Rio de Janeiro, RJ, Brazil
Abstract
With machine learning (ML) advances, many statistical solutions were developed to solve Sentiment
Analysis (SA) and Natural Language Processing (NLP) issues. However, no statistical classification
process was yet capable of solving simple semantic and linguistic relations in the same way as the human
brain. This work presents a brief review of ontologies for SA. The paper aims to raise discussions about
possible expansion strategies for the SA field and reflections on taking a deeper look at digital humanities
and its hybrid approaches, including ontologies, in the Brazilian-Portuguese scenario.
Keywords
Natural Language Processing, Sentiment Analysis, Ontology, Semantics
1. Introduction
With the rise of Web 2.0, social networks and the empowerment of users on the Internet, the
number of User-Generated Content (UGC) became an ever-expanding hoard of information
far exceeding human processing capabilities. The sharing of opinions has become frequent;
therefore, UGC data highlights which topics are more relevant to the public, helping organiza-
tions understand consumers’ impressions about their products [1]. This problem has motivated
affective computing scientists to seek methods and tools to automate information retrieval and
knowledge extraction from Web repositories.
Despite the variety of resources and techniques for Sentiment Analysis (SA), most applications
are developed for English corpus [2]. Recently, new contributions have appeared in other
languages [2], but research with Portuguese corpora is still scarce in Brazil [2]. With a population
greater than 211 million, Brazil has one of the most present online communities and the second
most active on Twitter [3], generating a large amount of textual data that justifies deeper looks
at its linguistic scenario.
Motivated by the studies of da Silva Conrado et al. [4] and Cambria et al. [5], our research
revealed that there is an important gap in the improvement of theories, methodologies, and even
in the state-of-the-art of linguistics and affective computing: the absence of studies focusing on
Proceedings of the 15th Seminar on Ontology Research in Brazil (ONTOBRAS) and 6th Doctoral and Masters Consortium
on Ontologies (WTDO), November 22-25, 2022.
$ erica.silva@aluno.cefet-rj.br (E. Carneiro); gustavo.silva@cefet-rj.br (G. Guedes); kele.belloze@cefet-rj.br
(K. Belloze)
0000-0002-6282-1344 (E. Carneiro); 0000-0001-8593-1506 (G. Guedes); 0000-0001-6257-2520 (K. Belloze)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
1613-0073
CEURWorkshopProceedingshttp://ceur-ws.orgISSN
ontology-based approaches for SA. Furthermore, most applications and tests used in affective
computing in Brazil employ automatic translations from English to Portuguese, whose linguistic
structure differs from Brazilian-Portuguese [4].
According to Gruber [6] [7], many contributions applying ontologies in the area of Artificial
Intelligence (AI) and information sciences exist. Advances in Machine Learning (ML) developed
many statistical solutions to solve SA and Natural Language Process (NLP) issues. However, no
statistical classification process could solve semantic and linguistic relations in the same way as
human thinking.
Da Silva Conrado et al. [4] observed that many scientific contributions in NLP in Brazil are
lexicons, excluding more holistic and semantic approaches, as we present in Table 1 (Section 3).
Also, according to Cambria et al.[5], NLP and SA should not be treated as merely classification
problems. In addition, our studies verify that, globally, most ontology-based approaches for
SA focus on specific domains, such as smartphones or diseases. Unlike most studies, SenticNet
is one of the unusual cases where SA was not applied to a specific domain but still uses an
ontology-based approach.
Senticnet [8] is a publicly available opinion mining resource using Semantic Web and AI
approaches. It deduces the polarity connected to common sense ideas. The framework represents
it in a semantically informed manner by generating a collection of commonly used common sense
"polar concepts" with relatively strong positive or negative polarity. SenticNet 6 SA classification
output is based on a semantic NLP level rather than just syntactic [8]. Its knowledge-based
onto-pair structure, in OWL, represents well how its semantic network is built [8].
This paper holds a brief review of ontological methodologies for SA, but we will not exhaust
ourselves on domain ontologies due to the wide variety of subjects. We also tested SenticNet
6 in Portuguese to check the need for new technical ontology-based solutions for Brazilian-
Portuguese. We hope to raise more discussions about possible expansion strategies for the SA
area and reflections on taking a deeper look at the Brazilian scenario. We believe our studies
might contribute to the future development of new tools capable of solving complex semantic
problems.
Our research was conducted in Scopus and Scielo databases limited to the period 2016-2021.
The search string ((“sentiment analysis”) AND (ontology) AND (Portuguese)) only found three
articles. Later, we expanded our research to ((“sentiment analysis”) AND (ontology)), leading us
to different solutions that could be applied in the Brazilian-Portuguese scenario. the backward
snowballing methodology helped locate other relevant works. The research articles were chosen
based on: (a) ontology-based approaches for SA; (b) language of the corpora; (c) the possibility
of domain adaptation, especially in works within English datasets.
2. Ontologies and Sentiment Analysis
Currently, the use of ontologically structured systems has gained prominence in the field of AI,
as they help in the sharing and reuse of formal knowledge acquired between AI systems [6],
[9], [10], [11]. Statistical or ML techniques have solved the problem of constructing ontologies
from texts, although the most significant challenge still lies in the semantics [12].
Many authors rely on simple relations between the classes, attributes, and other members of
automated extracted ontologies that depend on advanced NLP techniques and human judgment.
However, high-performance sentiment ontologies require many synonyms and relationships
[13].
From a semiotical point of view, human beings always use natural language and their social
and cultural knowledge of the world, or domain, to understand messages [14]. Likewise,
ontologies also use formal language representations to describe document domains [15].
The Suggested Upper Merged Ontology (SUMO) is an upper ontology with general concepts
independent of any domain. Its structure could be used to create domain-specific ontologies
related to sentiments. Niles et al. [16] mapped SUMO concepts to semantically equivalent
words, named synsets, from WordNet [16]. According to Saranya and Jayanthy [9], a solution
for emotion classification problems would be an ontological approach based on a given domain,
bringing semantic meaning and relationship between the terms. For Jiang et al. [17], an
ontological framework could perform better than pre-existing SA lexical approach. For the
researchers, the most efficient ontologies use solutions that combine polarity analysis and ML.
Hence, ontology learning and ML techniques for knowledge acquisition are fundamental in
developing research in the semantic web [10]. In addition, the use of NLP toolkits and lexical
databases for synsets analysis, i.e., a pre-existing synonym base, such as Wordnet, should be
considered when developing a SA tool with ontological structure [9].
According to Liu et al. [10], domain ontologies create relationships between a given domain
and its concepts. Therefore, the answer to solving possible classification failures in SA in
specific contexts could lie in them. An ontological approach might solve sentiment classification
problems based on a given domain due to semantic meaning and relations in the corpus [9].
Therefore, they could fix possible classification failures, such as ambiguation problems [10].
Ontology-based approaches could be of great value when applied in different areas for user
review analysis from various sectors of society. However, until the writing of this article, few
studies using ontologies for SA were developed, especially in the Brazilian scenario.
3. Sentiment Ontology Approaches
There are seldom ontologies addressed to the sentiment domain for text classifications. However,
we found some ontology-based sentiment opinion mining methods from different and specific
domains. We highlight some of them in this section.
English is usually the most common language for SA. In contrast to other idioms, ontology-
based approaches using English corpus are predominant. Some of them could influence new
works in different languages.
Kontopoulos et al. [18] created a prototype of a SA ontology focused on concept discovery.
The corpus consisted of English tweets covering the smartphone domain. The ontology uses
ML techniques and has hierarchical relationships and classes between concepts. As tweets are
composed of representative words and have syntactic content, text-based sentiment classifiers
are generally inefficient in their analysis due to the imposed character limit [18]. Hence, the
authors developed an onto-based technique whose sentiment scores are assigned to distinct
notions in the text. Its architecture identifies different sentiments in a single tweet.
Ceci et al. [19] developed a camera ontology domain based on Amazon text reviews. The
authors used the Movie Ontology for term classification purposes and sentiment learning on
product reviews with sentiment ontology tree [19]. Support Vector Machine (SVM) and Naïve
Bayes were employed for classification with the case-based reasoning approach. Ceci et al. [19]
analyzed the sentiment of product rating.
Based on Plutchik’s emotion wheel [20], the goal of Visual Sentiment Ontology (VSO) was
to design an ontology of semantic concepts related to emotions frequently shared on Flickr or
YouTube. Currently, the tool has more than 3,000 concepts composed of adjective-noun pairs,
in English, such as “beautiful sky” [21].
The Emotion Ontology (MFOEM) [22] is a subpart of the Basic Formal Ontology (BFO) and
the Ontology of Mental Functioning (MF). They include information and relations between
data types such as neuroimaging, pharmaceutical studies, behavior monitoring, etc. Despite
its immense scientific contribution, MFOEM will not classify sentiments at the sentence level.
However, its rich emotional vocabulary can be used to detect mentions of particular sentiments
through text mining approaches.
Ontology-based frameworks in English could be tested or even trained for other languages.
Senpy is a framework in Python that analyzes sentiments with a plugin architecture. It offers
a semantic web approach using semantic vocabularies. Senpy contains knowledge bases and
lexicons, including Vader, SenticNet, and Wordnet Affect. Despite recognizing languages, the
tool does not support Portuguese [23].
OntoSenticNet [24] is an ontological approach application for SA based on fuzzy ontology
methods and polarity verification of primitive concepts [11]. The tool is a feature of SenticNet,
a semantic repository with 100,000 terms in multiple languages. One central differential and
characteristic of Senticnet and OntoSenticNet is their conceptual hierarchical relationship,
whose properties relate to concepts and sentiment values. The tool also uses linguistic and
statistical analysis methods, deep learning and symbolic and subsymbolic AI. SenticNet can
extract Primitives using ML algorithms such as LSTM. The deep learning model allows the
automatic discovery of clusters of semantically related concepts sharing similar lexical functions
[25]. Its primary language is English, but the tool has multilingual support, including Portuguese.
Asian countries, especially China and India, have improved new research on ontologies
applied to SA. From entity extraction to the reuse of upper ontologies, there are remarkable
works in development in Asia. For Liu et al. [10], labeling product reviews with attributes and
their corresponding sentiment structure would be a way to perform SA. Hence, the authors
proposed a fuzzy domain ontological tree algorithm combined with a sentiment ontology
mechanism. It enabled the automatic building of a domain ontology tree based on product
reviews. Their method includes the sentimental terms extraction, product features, and their
relation. The corpus consisted of reviews from the Chinese website 360buy.com. According to
Liu et al. [10], their experiments improved the accuracy of polarity predictions.
Jung et al. [13] constructed their domain based on competency questions. The concepts and
their terminologies were collected from clinical practice guidelines, literature, and social media
posts about teenage depression. Also, the concept classes, hierarchy, and relationship-mapped
concepts are extracted from frequently asked questions (FAQ) answers. The ontology super
classes connect to the BFO with Protégé. The ontology application was in English, and its
dataset was in South Korean, whose terminology had 1,682 synonyms in the 443 classes.
Latin American countries contribute little to the state-of-the-art Spanish and Portuguese
idioms. We found only two works for the Brazilian-Portuguese language using ontologies
applied to SA. García-Díaz et al. [26] developed a SA method based on ontological aspects.
Their goal was to classify the polarity of emotions evoked by the epidemics of Dengue, Zika
and Chikungunya in Latin America. The corpus was compiled from Twitter and later labeled by
volunteers as positive, negative, and neutral. The proposed ontology collected its knowledge
from Disease Ontology (DO) and Infectious Diseases Ontology (IDO). The DO and IDO do
not have Spanish versions. Therefore, the terms were manually included, which was highly
time-consuming. Due to domain specificity, the authors dealt with many term ambiguity issues.
Despite manual translations, García-Díaz et al. [26] failed to solve disambiguation problems
effectively.
Freitas and Vieira [27] developed a tool to identify features in the hotelier domain. The
authors also noted the lack of resources for the Portuguese language. Thus, the work needed
several evaluation stages for its conclusion. The dataset consists of 194 TripAdvisor reviews
published from March 2010 to May 2014. HOntology has 282 concepts categorized into 16
higher-level concepts.
KBRS is a knowledge-based recommendation system with an emotional health monitoring
application to detect users’ depression and stress. Depending on the monitoring results, the
KBRS, based on ontologies and SA, is activated to send motivational messages [28]. Recurrent
neural networks are responsible for identifying sentences with depressive and stressful contexts.
According to Rosa et al. [28], experimental results suggest that the proposed KBRS achieves a
rating of 94% of satisfied users compared to 69% for recommendation systems without sentiment
and ontology metrics.
The assemblage of those works leads us to think that research using ontology-based ap-
proaches is not the primary option for sentiment classification. As stated in Section 1,
da Silva Conrado et al. [4] already observed that most scientific contributions in SA in Brazil
are lexicons that treat SA as a classification problem, regardless of the corpus semantic relations
in the sentences, therefore, excluding more holistic analysis in their approaches. Traditionally,
lexicons are a set of words in a given language. From a computational SA perspective, such
groups of words are related, presenting different labels for possible sentiments they may express.
Table 1 lists some of the most important studies in Brazilian-Portuguese. The table indicates
if the study applied a translation or if it was originally developed in Portuguese. Nineteen
significant studies with a focus on SA were discovered. Of 10 ontologies-based works, only two
had an SA focus. Nine works were SA lexicons-based. The Table shows the title of the study, its
approach and original language, treatment of language problem, sentiments or other specific
domains, and the year of the study. As SenticNet is a multilanguage tool without Portuguese
research, we did not include it in Table 1.
4. Experiments
Our preliminary tests aimed to support the validation of further research in the field of SA with
ontological and semantic approaches. As SenticNet is a multilingual open-source framework
with support for the Portuguese language, we employed it to evaluate the effectiveness of
automatic translations for emotion classification and the performance of its ontological pairs. In
SenticNet, each concept 𝑐 is associated with a Polarity value 𝑃 for 𝑐, i.e a floating value between
[−1, 1], representing its polarity.
We tested four datasets in Portuguese: MQD [29], SADT [30], TOPIE [31] and TweetSentBR
[32]. SenticNet recommends not preprocessing the corpus before input. The tests were per-
formed in Python. Senticnet 6 results for English datasets were greater than 80% [25], our
Portuguese tests outcome were circa 30%: MQD [29] (37%), SADT [30] (32%), TOPIE [31] (31%),
and TweetSentBR [32] (36%). The tests exposed the relatively low accuracy for automatic Por-
tuguese translations applying Senticnet 6. This fact raises the discussion about the differences
in idiomatic structures, especially when dealing with semantic relations.
Table 1
A comparison between lexical and onto-based approaches for Affective Computing in Brazil According
to Original Language and Domain (*EN = English; PT = Portuguese; BPT = Brazilian Portuguese)
Original
Study Title Approach Type SA Approach Domain Year
Language*
ANEW BR [33] Lexicon EN Translation X N/A 2011
LIWC [34] Lexicon EN Translation X N/A 2007
LIWC 2015pt [35] Lexicon EN Translation X N/A 2015
Wordnet AffectBR [36] Lexicon EN Human Translation X N/A 2003
Opinion lexicon [37] Lexicon EN API Translation X N/A 2011
Wordnet AffectBR_adapt [38] Lexicon EN Human Translation X N/A 2011
Reli Lexicon [39] Lexicon PT Brazilian Idiomatic Matrix X N/A 2013
Personalitem Lexicon [40] Lexicon BPT Brazilian Idiomatic Matrix X N/A 2015
Unilex [41] Lexicon BPT Brazilian Idiomatic Matrix X N/A 2017
Brazilian and European
Linguateca [42] Ontology PT Linguistics 1998
Idiomatic Matrix
TEXTQUIM/lTEXTECC [43] Ontology BPT Brazilian Portuguese Chemistry/Medicine 2003
NANOTERM [44] Ontology BPT Brazilian Portuguese Nanotechnoligies 2006
OntoLP [45] Ontology BPT N/A Semi-Automatic Ontology Builder 2008
Bio-C [46] Ontology BPT Brazilian Portuguese Bio-Fuel 2009
e-Termos [47] Ontology BPT Brazilian Portuguese Electronics 2009
TermiNet [48] Ontology BPT Human Translation Linguistics 2009
Hontology [27] Ontology BPT Brazilian Portuguese X Accomodation Sector 2012
Chemistry, Computing,
TOPTAX [49] Ontology BPT Brazilian Portuguese 2013
Physics, IFM
KBRS [50] Ontology BPT Brazilian Portuguese X Depression and Stress 2018
5. Final Remarks
This paper reviewed ontologies applied to SA and raised some reflections about possible expan-
sion strategies for the SA field in Brazil. Although there are novel ontology approaches, with
highlights to the work of [28] and [27], most research for Brazilian Portuguese focus on lexicon
approaches that reduce NLP and SA to a simple classification problem. Several studies abroad,
especially in English, have already testified that more elaborate linguistic structures require
more robust text-mining tools [10].
This research is still under development, but we hope this Brazilian ontology review will
evoke new concerns and studies on the NLP and SA Brazilian academic field. For future works,
we expect to develop a sentiment ontology prototype based on the research of [51], and [28]
and then conduct new experiments.
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