A Model for Describing and Classifying Sentiment Analysis Methods
Aleksander Waloszek and Wojciech Waloszek
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland,
Keywords: Sentiment Analysis, Opinion Mining, Knowledge Management, Ontologies.
Abstract: In this paper we introduce SACAM — a model for describing and classifying sentiment analysis (SA)
methods. The model focuses on the knowledge used during processing textual opinions. SACAM was
designed to create informative descriptions of SA methods (or classes of SA methods) and is strongly
integrated with its accompanying graphical notation suited for presenting the descriptions in diagrammatical
form. The paper discusses applications of SACAM and shows directions of its further development.
This paper presents a novel model of describing
methods of sentiment analysis. Sentiment analysis is
a very quickly evolving field of research, and focuses
on assessing emotional attitudes expressed in textual
opinions contained in various documents.
The model has been developed within a research
project conducted by academia in cooperation with
industry and placed in the field of sentiment analysis
and knowledge management, representation and
reasoning. The main goal of the project was to bring
closer those two fields of research and to use formal
ontologies in sentiment analysis.
Planning of project tasks and designing new
methods of sentiment analysis involve frequent
references to existing methods. To facilitate the task
and to make its result easier to present and examine,
we developed a novel model of describing and
classifying sentiment analysis methods. The method
is called SACAM, Sentiment Analysis Content
Awareness Model.
The purpose of the model is to provide means for
concise graphical description of sentiment analysis
methods. The model is well-suited for describing both
particular methods and classes of them. Graphical
notation provided with the model is designed to
underline the crucial aspects from the point of view
of the managing knowledge during analysis:
knowledge repositories used within the method, the
methods of building or augmenting such repositories,
stages and means of the processing.
Use of the model allowed us for easier navigation
in the hard field of sentiment analysis, whose rapid
evolvement results with at least several hundreds of
notable papers appearing every year. It also permitted
us to precisely pinpoint the area in which we place
our future efforts, and to plan further actions within
the project.
The paper is focused on presenting SACAM
model, and while the next Section provides some
essential information about sentiment analysis, the
paper should not be treated as a survey in the field.
This role is fulfilled by some excellent existing papers
and books like (Pang and Lee, 2008), (Cambria et al.,
2013) and (Liu, 2012).
The rest of the paper introduces the model, shows
examples of its use, and discusses its potential
applications and directions of development.
This Section provides a short introduction to
sentiment analysis field (Section 2.1) and the existing
approaches to classifying sentiment analysis methods
(Section 2.2). It draws a background for introducing
the SACAM model.
2.1 Introduction to SA
Sentiment analysis (SA) evolved and separated itself
from the fields of Natural Language Processing and
Affective Computing in early 2000. The term itself
Waloszek A. and Waloszek W.
SACAM - A Model for Describing and Classifying Sentiment Analysis Methods.
DOI: 10.5220/0006199901960206
In Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART 2017), pages 196-206
ISBN: 978-989-758-220-2
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
appeared in 2003 in (Nasukawa and Yi, 2003), in the
same year another prominent name for the domain
opinion mining was proposed in (Dave et al., 2003).
Both the terms sentiment analysis and opinion mining
are used in the literature, along with many related,
though a bit more specific, terms like review mining,
opinion affect analysis, sentiment mining or emotion
analysis (Liu, 2012).
Sentiment analysis from its beginning focuses on
extracting information about users’ emotional
attitudes from large corpora of documents, especially
from social media. In comparison with its ancestor
fields (NLP and affective computing) the problems
here are approached more directly and specifically.
The researchers do not focus on creating methods for
perfect understanding of texts being analyzed. The
texts are very often being treated as bags-of-words
exposing some features based on presence or absence
of specific words (or their co-presence expressed as
n-grams—pairs, triples, etc. of words). Also the
emotions exposed in the texts are typically not
identified very comprehensively. The usual outcome
of the analysis is bipolar: emotions are identified as
positive or negative (sometimes neutral).
The range of phenomena being analyzed is quite
broad, and includes sentiments, emotions,
evaluations, and attitudes towards products, services,
organizations, persons, events, news etc. However,
there exists no tool suitable for handling all those
phenomena universally, and most of algorithms
developed in the field focus on a single problem:
specific type of text, like microblog entry, and
specific object being evaluated, like a tablet or a
mobile phone.
The strength of methods of sentiment analysis
most frequently stems from their statistical character.
For instance, presence of the word “excellent” in a
text may be treated as a sign of the text bearing
positive opinion. This rule, while in some (perhaps
many) cases not true, when applied to a large corpora
of texts may turn out to be feasible enough to
positively contribute to extracted information about
expressed opinions.
Attention drawn by the subject of sentiment
analysis increased rapidly, from purely scientific
interest, towards many applied methods. Currently
most of the companies involved in business
intelligence (like Microsoft or SAS) offer their own
solutions for opinion mining. One of the reasons is
very broad range of potential applications: sentiment
analysis has been used for assessing sales volume
(Liu et al., 2007), ranking sellers and products
(McGlohon et al., 2010), prognosis of movie box
office (Asur and Hubeman, 2010) or assessing
attitudes of stock exchange investors ((Bollen et al.,
2011) on the basis of tweets, (Bar-Haim et al. 2011)
using posts in expert microblogs). Semantic analysis
found its applications also in political debate
(Tumasjan et al. 2010, Chen et al. 2010) to predict the
results of presidential vote in the USA.
2.2 Classifying SA Methods
The field of sentiment analysis is very rich and many
papers in the domain contain proposals of
classification schemes for SA methods. Such
proposals are most frequently presented in survey
papers and books reviewing the field, and can be used
to underpin some of the most important
characteristics of methods being classified.
One of the most classic decompositions of the
methods in the field was presented in (Feldman,
2013). The methods are classified along two
dimensions. The first dimension is about granularity,
i.e. the degree into which a method investigates the
contents of a document. While not precisely
distinguished in (Feldman, 2013), one can order those
degrees into the following hierarchy:
Document-level analysis,
Sentence-level analysis,
Aspect-level analysis.
Analysis at a document level is the most
straightforward way of assessing sentiment. Methods
at this level assign sentiment orientation to whole
documents, most frequently in the bipolar form of
positive/negative score. Sentence-level analysis
consists in assigning orientation to subsequent
sentences. Working at this level might be helpful in
detecting mixed opinions about the object of
sentiment, and is also useful when some special kinds
of sentences should be treated in a special way (like,
simply, filtering out some sentences, say sarcastic
ones). Aspect-level analysis allows for assigning
sentiment not only directly to the object being
assessed but also to its “parts”, known as aspects.
Aspects need not to be necessarily physical parts of
the object, they may also refer to its features (like
“display quality”). Assessing at aspect level allows
for assigning sentiment score to parts and features of
the object and, consequently, allows to extract
interesting information also from mixed opinions. At
the end of such analysis user may be presented with
more detailed report with score for each of the
Analysis-level dimension is augmented by the
division of methods by the learning technique
applied. We distinguish here supervised and
unsupervised methods. In supervised learning we
SACAM - A Model for Describing and Classifying Sentiment Analysis Methods
assume that we have at our disposal a training set that
has been already labeled (e.g. by a human expert). For
instance in the training set we may have examples,
each representing a single review, with features like
n-grams and labels in the form of positive/negative.
From this set, a machine learning algorithm derives
rules of assigning each example (and new examples)
to the distinguished classes. Many data mining
methods are suitable for this purpose, like Decision
Trees, Naïve Bayes Classifiers, Support Vector
Machines etc.
In unsupervised methods we do not have any
labeling ready. Instead the algorithm, according to
some rules, has to extract some features or parts of the
document. An example may be syntactic rules for
finding phrases in Turney’s method (see Section 3.2).
This kind of learning usually requires the existence of
some kind of sentiment lexicon, i.e. a set of reference
words with assigned information about strength and
polarization of the sentiment expressed by them.
The two mentioned dimensions are most
frequently used in classification. Many other methods
of classification are based on those dimensions and
extend them by new ones. An example is (Cambria et
al., 2013) whose authors (apart from adapting
granularity dimension) propose two new dimensions:
discourse, and conceptual.
Discourse dimension refers to the level of
awareness of the structure of discourse presented
throughout the document. Most of the methods ignore
this structure, e.g. by treating all the phrases equally
regardless of the context of their appearance and their
position in the document. Simple mechanisms that
exhibit some discourse awareness may, for instance,
detect summaries presented at the beginning and at
the end of the document, and treat phrases in these
sections differently (e.g. with higher weight). Most
advanced mechanisms should be able to detect
citations, quoted opinions, sarcastic answers or
examples, in order to properly interpret the sentiment
of specific phrases of sentences, or maybe even just
to exclude some kinds of them from further analysis.
Conceptual dimension refers to mechanisms used
for extract sentiment, and order them in accordance to
the ability of extracting the meaning of word and
phrases. This conceptual dimension is also very much
connected to the ability of interpreting context of the
words and phrases being examined. While simple
methods rely on a keyword list with sentiment
explicitly assigned, more advanced methods may
treat sentiment carried by a word or a phrase more
cautiously, for example by assigning them
probabilities of expressing positive and negative
opinion. Even more advanced mechanisms may take
into consideration co-occurrence of the terms and
their position in the sentence (e.g. by considering also
punctuation). Most advanced methods along this
dimension can be equipped with a knowledge base
about concepts in the domain of interest (like the
knowledge about the construction of a mobile phone)
and may be able to use this knowledge for in-depth
analysis of the sentences. Quoting (Cambria et al.,
2013): “Concept-based approaches can analyze
multi-word expressions that don’t explicitly convey
emotion, but are related to concepts that do”.
Classification schemes very often refer also to the
particular mechanisms included in the method (Liu,
2012, Cambria et al., 2013, Feldman, 2013). The list
of the mechanisms is more or less predetermined and
often includes features like presence or absence of
sentiment lexicon (set of word with assigned
information about sentiment carried), aspect lexicon
(expressions about feature of parts of object being
assessed), ability to identify special sentences (like
sarcastic ones, or objective sentences telling us about
facts), ability to identify special entities (like proper
names) etc.
Apart from dimensions and mechanisms
(Feldman, 2013) also describes the general
architecture of a sentiment analysis method, and
treats this as a useful resource during classification.
The architectural schema proposed by (Feldman,
2013; with minor changes mostly due to other
graphical layout of the paper) is presented in Figure
Figure 1: A general architecture of a sentiment analysis
method on the basis of (Feldman, 2013).
Input for a method is a corpus of documents,
which can be pre-processed in order to create or refine
the contents of additional resources (e.g. lexicons).
The resources are then used for proper document
ICAART 2017 - 9th International Conference on Agents and Artificial Intelligence
analysis and to produce sentiment score. Important
thing worth noting in the picture is the recurrent
character of the flow, which may consist of stages
whose execution might be repeated during the course
of the method.
This Section describes the SACAM model (Section
3.1) and gives an example of its use, based on one of
the very widely known Turney’s method (Turney,
2002) for sentiment analysis (Section 3.2).
3.1 General Rules of SACAM
The classification schemes described in Section 2.2
certainly underpin some of their interesting of SA
methods being classified, and can be treated as base
for creating their formal and abstract descriptions.
Considering, however, the discrepancies between
various classification schemes, and the fact that no
survey or book proposed a detailed method of such
formal description, we decided to build a new model,
SACAM, on the top of the selected existing
classification schemes.
Driven by the requirements of the project,
mentioned in the Introduction, we strived for a model
for describing sentiment analysis methods
particularly focusing on processing knowledge
during the analysis of textual opinions. All the
methods of sentiment analysis base on knowledge
about the content being processed. This knowledge
can be subdivided into several areas: knowledge
about grammatical structure of the texts being
analyzed, knowledge about meaning of specific
words, and knowledge about the domain of interest,
i.e. the object being assessed. In our classification we
wanted to focus on how the knowledge in these (and
perhaps additional) areas is used, stored and
processed. This is the reason why we called the new
method Sentiment Analysis Content Awareness
Model (SACAM).
What we wanted to achieve was the model for
describing methods of sentiment analysis. The
descriptions prepared in accordance to the model
should give clear and immediate clues about the way
of processing knowledge during the analysis,
especially should answer the questions like: whether
the knowledge in aforementioned areas is taken into
consideration at all, is it statically programmed into
method, or maybe prepared in some preliminary
stages, is it expressed explicitly or is implicitly
contained in some components. As it can be seen
from this description, the structure of the process
itself was also very important to us. Apart from these
properties, we also expected the method to expose
such characteristics as how much human expert work
is needed.
Another very important requirement for the model
was the ability to describe not only single specific
methods of sentiment analysis, but also whole
families of methods. In this way we could also obtain
the tools for classifying methods and to compare them
on the more aggregated basis.
Taking these requirements into consideration we
developed a graphical notation being the core of the
SACAM model. Using this notation one can prepare
a diagram being a description for a method or a family
of methods of sentiment analysis. The notation is
based on standard block diagrams being used to
depict business processes in an organization.
Each diagram consists of standard elements depicted
in Figure 2. We drew inspiration here from the work
(Feldman, 2013) which also borrowed such elements
to describe the general process of sentiment analysis
(see Figure 1). The meaning of each of the elements
was slightly changed in order to express specifics of
sentiment analysis.
Figure 2: Basic elements of a SACAM diagram.
The elements from Figure 2 can be combined into
a description of a flow. This flow depicts the main
steps taken in the described method. The flow may be
divided into several stages, differentiated by (not
necessarily disjoint) frames. It should end with the
terminal symbol, representing the final outcome of
the algorithm.
The SACAM diagrams come in two flavors. A
diagram may depict a single method (particular
diagram), or a family of methods (generic diagram).
In the first case, the elements of the diagram refer to
repositories used and algorithms executed by the
particular method. In the second case the elements
represent more generic repositories and steps, and are
a placeholder for filling by a more specific family (or
SACAM - A Model for Describing and Classifying Sentiment Analysis Methods
a specific method). An assumption is made here, that
this is not necessary (for the more specific family or
method) to fill all the placeholders.
Such an approach allows us to create a diagram
that captures the broadest family of almost all typical
methods of sentiment analysis. This general diagram
(called a root diagram) is shown in Figure 3.
The root diagram shows the three kinds of
knowledge repositories used by typical methods of
sentiment analysis: repository of processing rules
(including use of grammar, e.g. mechanisms like
POS-taggers), repository of knowledge about
sentiment words (sentiment lexicons) and repository
of knowledge about the object of assessment (aspect
lexicons). The three repositories are depicted by three
vertical flows (diagram “columns”).
Sentiment lexicons and aspect lexicons (SL, AL)
are traditionally understood repositories, depicted by
an appropriate symbol. For such repositories there
often might be a special process of their building
(learning stage). Most commonly some preliminary
form of a lexicon is given (called seed lexicon; SSL,
SAL). Seed lexicon is being iteratively extended
during the process. The seed forms of lexicons are
depicted in the first row of the diagram, and the
iterative process of building the final form of the
repositories is illustrated by the upper frame. The
frame is “plural”, which is indicated by additional
incomplete rectangles extending beyond the main
frame. The plural form of the frame indicates that the
stage may be repeated a number of times.
The knowledge about processing is represented in
the form of repository of rules (PR). It plays the major
role in forming the main loop of an algorithm being
described. This kind of knowledge is “operational”,
as it constitutes the flow in the process of generating
the outcome (final assessment). However, this does
not necessarily mean that the exact form of rules
needs to be known in advance: the processing rules
may be also generated or made more specific during
the learning stages. As those rules form the low-level
knowledge about processing documents in general,
they might be also used (sometimes in a modified
form) to generate sentiment and aspect lexicons
(SPR, APR) and sometimes to refine the processing
rules themselves (RPR; PR generally consists of
RPR, SPR and RPR which is represented in the
diagram with a dotted line). Processing rule very
often embrace basic or more advanced knowledge
about grammar, as many of the sentiment analysis
Figure 3: The root diagram of SACAM model, with the legend for abbreviations in the bottom right corner.
ICAART 2017 - 9th International Conference on Agents and Artificial Intelligence
methods perform various actions on the basis of
grammatical properties of encountered words or
sequences of words (like Part-of-Speech, PoS).
Solid arrows show flow of knowledge in the
method. Arrows directed towards processes show that
a process uses a repository (documents), arrows
directed outside processes show its outcome (final
outcome or contribution to a repository). Dashed line
show a feedback loop within a plural stage. As
mentioned above, dotted line show that some
repository may consists of other repositories.
Root diagram is a general SACAM diagram,
which means it can be specialized to show a specific
method, or a narrower family of methods. When
specializing a general diagram, the more specific
diagram reuses the subset of the elements from the
more general one, and augments them with
comments. The comments are placed within the
elements and for the processes they should normally
If the process involves human work;
If the process requires external applications and/or
What algorithms are used.
While for repositories they should indicate:
What does the repository contain;
How the contents are represented.
A person creating the diagram naturally has some
freedom in selecting the most important features to be
described, but they should generally follow the main
principle of SACAM: to depict the flow of
knowledge. Therefore, all the information concerning
knowledge acquisition, representation and processing
should be a priority.
3.2 Creating a Particular SACAM
Diagram for Turney’s Method
In this Section we show how to construct a particular
SACAM diagram, taking as an example one of the
most widely known SA methods, Turney’s method,
described in (Turney, 2002). In general the method
uses unsupervised learning for analyzing texts on the
basis of syntactical patterns for expressing opinions.
The syntactical patterns used in the algorithm
reflect the observation that different parts of speech
have different influence on expressed opinions. The
most important (i.e. those whose sentiment is mostly
correlated with the overall sentiment) are adjectives
and adverbs.
The syntactical patterns are used in Turney’s
method to find phrases of interest. The patterns (and
phrases) consist of two consecutive words
(sometimes with a third following word not included
into the phrase) of appropriate part-of-speech. An
example of a pattern expressed with tags (Penn
Treebank Project, 2016) is: JJ NN/NNS, which means
that it matches adjectives followed by nouns in plural
or singular form.
The main algorithm accepts a set of reviews S and
consists of three stages. In the first stage the algorithm
looks for matching phrases from S. In the second
stage sentiment orientation of each phrase is
determined. For this task a measure called pointwise
mutual information (PMI) is used, which, for two
different phrases, is calculated with the equation:
, phrase
) = log
) (phrase
where p(phrase
) is the probability of co-
occurrence of the two phrases, where p(phrase
) is the product of probabilities of occurrence
of the two phrases (or also co-occurrence, if they are
The algorithm uses this equation to determine “the
distance” between the matched phrase (phrase
) and
the two predetermined phrases “poor” and
“excellent”. The sentiment orientation (SO) of the
phrase is then calculated as:
which can be transformed into:
) = log
excellent) p(poor)
poor) p(excellent)
The probabilities of occurrence and co-occurrence of
phrases are unknown. But in Turney’s method they
are estimated with clever use of Internet search
engine (Turney used AltaVista): p(phrase) is
estimated as proportional to number of hits for the
phrase, while p(phrase
) as proportional to
number of hits for the query phrase
NEAR phrase
This allows for further transformation of the equation
) = log
NEAR excellent)
NEAR poor)
where hits denotes the number of hits returned by the
search engine.
In the third and final stage SO of each review from
the set S is determined as the average of SO of all
phrases extracted from this review. Positive number
indicates a positive sentiment orientation.
Fig. 4 contains the description of Turney’s method
in the form of a particular SACAM diagram (as a
SACAM - A Model for Describing and Classifying Sentiment Analysis Methods
specialization of the root diagram). The primary
features of the diagram are expressed with its shape.
Along the horizontal axis we can see that the Turney’s
method exploits two repositories: of grammatical
(processing) rules and a sentiment lexicon.
Figure 4: Turney’s method described with a SACAM
Along the vertical axis we can see that the
sentiment lexicon needs to be prepared, and is used
for the proper assessment. The process of creation the
sentiment lexicon is virtual, which means that the
lexicon is not materialized, and the learning stage is
in fact intertwined with the process of classification.
Nevertheless, the knowledge about sentiment of
specific words is very strongly present in Turney’s
method. In addition, it is based on large corpora of
documents indexed by AltaVista, which is shown by
the secondary features, i.e. features that are described
in the comments.
In this Section we present applications of SACAM,
including already performed by us (Sections 4.1, 4.2,
and 4.3) and further possible applications (Section
4.1 Roadmap of SA Methods
One of the most straightforward uses of SACAM is
to depict classes of existing SA methods. Within our
project we undertook such a task and created general
diagrams for top-level classes of methods, basing
mainly on the division presented in (Liu, 2012).
Created diagrams, among others, depicted:
Supervised and unsupervised SA methods;
Methods involving detection of subjective
Methods involving creation or use of sentiment
Methods involving creation or use of aspect
Figure 5: SACAM diagram for supervised SA methods.
Due to constrained space for the paper we only
reproduce here diagrams for supervised methods
(Figure 5) and for methods involving creation of
sentiment lexicon (Figure 6).
As diagrams like those presented in the
aforementioned figures tend to increase in size, we
also introduced the notion of a partial SACAM
diagram, which highlights some of the fragments of
the flow within a method or a class of methods. An
example of such diagram is shown in Figure 7, where
a mechanism for creation of sentiment lexicon with
use of WordNet (Miller, 1995) is highlighted.
ICAART 2017 - 9th International Conference on Agents and Artificial Intelligence
Figure 6: General SACAM diagram for unsupervised
Figure 7: Partial SACAM diagram showing sentiment
lexicon creation with use of WordNet.
Diagrams like this can be useful to create a
general map of the field, depicting main classes of
methods and pointing out similarities and differences
between them. Individual methods can be matched
against general diagrams. For instance, Turney’s
method (traditionally counted in unsupervised
methods) fits almost perfectly into the schema for
methods involving sentiment lexicon, obviously with
the (very) notable exception that the lexicon in
Turney’s method is of purely virtual character.
Such a collection of general SACAM diagrams
can easily be maintained and augmented with the
appearance of new classes of SA methods.
4.2 Designing New SA Methods and
Comparing SA Methods
Particular SACAM diagrams are also a very efficient
tool of designing new SA methods and to compare
(newly designed or existing) methods to each other.
Consider an exemplary new method with the
following characteristics. The method consists of
three preliminary stages and two main steps. During
the preliminary stages the data for proper
classifications of reviews is prepared. We assume that
a given set of reviews of a product (any product, but
we may think of a mobile phone here) is available.
In the first preliminary stage, the topics of the
assessed reviews are automatically predetermined.
Latent Dirichlet Allocation (LDA; Blei et al. 2003),
revealing words most characteristic for each topic,
and in this way, indirectly, determine the set of
relevant topics. In the second preliminary stage the
collection of topics extracted during the first stage is
reviewed by experts. The experts identify phrases
which refer to qualitative aspects of a product (like
“display quality”, “memory size”, “energy
consumption”) and identify the aspects as “positive”
(like “display quality”), which means that larger
amount of this quality is desirable, or “negative” (like
“energy consumption”), which means that larger
amount of this quality is undesirable. In the third
preliminary stage the experts identify the words
(intensity words) that indicate higher amount or
intensity for each of aspects (like “high” for both
“display quality” and “energy consumption” and
“unacceptable” only for “energy consumption”)
along with words for lower intensity for each of
In the first main step, phrases that are
combinations of intensity words and aspects are
identified, and the sentiment orientation of each
phrase is determined on the basis of whether the
qualitative aspect itself is positive of negative and
whether the intensity word indicates high or low
intensity/amount of this aspect. In the second main
step the assessment of a single document is calculated
as a difference between the number of “positive”
phrases and “negative” phrases.
These assumptions are sufficient to allow us to
easily create a SACAM diagram for the method (see
Figure 8). The method can be easily compared to
other methods, like Turney’s, by checking the
primary and secondary features of the diagrams.
SACAM - A Model for Describing and Classifying Sentiment Analysis Methods
The differences can immediately be seen in
primary features: the new method uses an aspect
lexicon. The secondary features reveal more
distinctions: primarily the amount of experts’ work
needed in the alternative method, and concrete (non-
virtual) character of lexicons prepared there.
Naturally, there are also similarities between the
two methods. Both of them use knowledge about
sentiment carried by specific words (sentiment
lexicons), both need the lexicons to be prepared, and
both calculate the final score for the documents as an
aggregated score of extracted phrases.
4.3 Assessing SA Methods
SACAM diagrams may be used as a basis for creating
formal or semi-formal measures of fitness of
particular methods or classes of methods for a
specific task.
Within our project we used this approach to
estimate the best directions for integrating ontological
knowledge management with traditional sentiment
analysis. To this end we created a semi-formal
measure of semantic potential, whose value was
determined on the basis of presence or absence of
specific constructs in the SACAM diagrams.
While the detailed description of the measure is
outside of the scope of this paper, the basic factors
influencing the measure (positively) were the number
of knowledge repositories, presence of procedures for
building or augmenting the repositories, and
interactions between repositories, i.e. using the
contents of one of the repositories to refine the
contents of another repository. The last effect is
present, for instance, in the methods which build their
aspects lexicon on the basis of identification of
sentiment words (like e.g. (Blair-Goldensohn, 2008)
or (Somasundaran and Wiebe, 2009)), which
mechanism is depicted in SACAM in Figure 9.
Use of the aforementioned measure allowed us to
direct our interest towards methods of this level of
interaction between repositories. Ultimately, we
chose a bit different course of actions and decided to
use modular ontological knowledge bases to support
the process of creating customized sentiment lexicons
for each of the identified aspects. The measure,
developed using SACAM, allowed us to make a more
informed decision in this subject.
Figure 8: The root diagram of SACAM model, with the legend for abbreviations in the bottom right corner.
ICAART 2017 - 9th International Conference on Agents and Artificial Intelligence
Figure 9: Partial SACAM diagram showing use of
sentiment lexicon in building aspect lexicon.
4.4 Further Applications of SACAM
Another direction for development of SACAM
consists in laying more solid foundations for its
formalization. One of possible approaches here is to
create an ontology for the model. Ontologies are
formal specifications of conceptualizations in various
fields (Gruber, 1993). As such, they constitute a very
good instrument for formalizing all kinds of
descriptions. The state-of-the art language for
creating ontologies is OWL 2 (OWL 2, 2012).
Creation of a formal OWL ontology for SACAM
would create numerous possibilities. Firstly, it would
allow for automated verification and comparison of
SACAM descriptions. Secondly, it would be possible
to express in the ontology not only the descriptions of
methods and classes of methods but, for example, the
requirements for a method for a specific project. This
would allow for developing measures for fitness of
specific methods for specific tasks and, possibly,
automated search of methods.
In this paper we presented the SACAM model for
describing methods and families of methods of
sentiment analysis. The core of the model is a
graphical notation used to depict the flow of
knowledge during such analysis.
The graphical notation adopts the elements of
block diagrams used for describing business process.
Shape of each SACAM diagram expresses primary
features of a method (or family of methods), which
are the knowledge repositories used and the required
stages of processing. The secondary features, like
involvement of experts, way of knowledge
representation, and use of specific tools for
knowledge processing are revealed in the comments
present in each block.
The applications of SACAM are numerous.
Within our project SACAM proved itself a helpful
tool. It has been successfully used to describe both
various classes of methods and particular methods
and to compare them to each other. Practitioners in
the field may also find it a useful tool for designing
new methods. On the basis of the presence or absence
of some specific construct in the diagram one can
derive measures of choice for more detailed
assessment of SA methods and algorithms.
One of the most promising directions of use and
development of the model is to formalize it with use
of ontologies. It might lead to new uses of SACAM,
like automated verification and assessment of
sentiment analysis methods.
This work described in the paper was partially
supported by the Polish National Centre for Research
and Development (NCBiR) under Grant No.
PBS3/B3/35/2015, project “Structuring and
classification of Internet contents with prediction of
its dynamics” (Polish title: “Strukturyzacja
i klasyfikacja treści internetowych wraz z predykcją
ich dynamiki”).
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