i-SLOD: Towards an Infrastructure for Enabling the Dissemination
and Analysis of Sentiment Data
Rafael Berlanga, Dolores Mª Llidó, Lisette García, Victoria Nebot,
María José Aramburu and Ismael Sanz
Universitat Jaume I, Avda. Vicent Sos Baynat s/n, Castellón de la Plana, Spain
Keywords: Opinion Analysis, Open Linked Data, Business Intelligence.
Abstract: This paper proposes a new data infrastructure for massive opinion analysis, called i-SLOD, from a Business
Intelligence (BI) perspective. This infrastructure aims to allow analysts to re-use the existing review data
about products and services publicly available in the Web. It should also take advantage from the external
relationships of i-SLOD data in order to perform new exploratory analyses now unfeasible with traditional
BI tools. We consider the adoption of Linked Open Data (LOD) technology to build this infrastructure. In
this way, i-SLOD data will be published as distributed linked open data by using the RDF and OWL
formats. Moreover, we propose to apply automatic semantic annotation to perform the basic tasks in i-
SLOD, mainly the extraction of opinion facts from raw text, and linking opinion data to the i-SLOD and
other related LOD datasets.
1 INTRODUCTION
The massive publication of opinions about product
and services has produced a burst of methods for
sentiment analysis (Liu, 2012). Most of these
approaches directly deal with the review texts to
identify global assessments (reputation) of certain
products and services. They are mainly focused on
detecting the subject of the opinion (e.g., some
product or some aspect of it) as well as the
orientation of the opinion (i.e., polarity). Massive
mining of opinions allow obtaining good indicators
about the Voice of the Market (García-Moya et al.,
2013a). Due to the high interest of this kind of data,
a good number of commercial tools have recently
appeared in the market, for example Swotti, Radian6
Insight, Media Miser, Scout Labs, Wise Window
and Sinthesio, to mention a few. Unfortunately, most
of these tools just provide web reports targeted to
end-users, and the sentiment data is not publicly
available for third party applications.
Apart from the sentiment analysis approaches,
there is also a great interest on publishing strategic
data for Business Intelligence (BI) tasks within the
Linked Open Data (LOD) cloud (Heath and Bizer,
2011). Initatives like Schema.org are allowing the
massive publication of product offers as microdata,
as well as specific vocabularies for e-commerce
applications. Unfortunately, both worlds, sentiment
data and LOD technology, have kept unconnected to
each other until recently. Some preliminary projects
such as MARL (Westerski and Iglesias, 2011)
attempt to provide standarized schemas for
expressing opinion data as linked data. However,
nowadays there is no open data infrastructure that
allows users and applications to directly perform
analysis tasks over huge amounts of published
opinions in the Web.
In this paper, we propose i-SLOD, a new data
infrastructure for sentiment data aimed at satisfying
the necessity of generating and analysing opinion
data from a BI perspective in the context of the LOD
initative.
2 i-SLOD ROAD MAP
Traditional BI assumes the existence of a controlled
set of data sources, from which summarized data is
obtained for decision making tasks. BI architectures
usually rely on a data warehouse defined under a
multidimensional model (i.e., just consisting of
measures and dimensions), which is fed with data
extracted from existing data sources by applying the
214
Berlanga R., M
a
Llidó D., García L., Nebot V., José Aramburu M. and Sanz I..
i-SLOD: Towards an Infrastructure for Enabling the Dissemination and Analysis of Sentiment Data.
DOI: 10.5220/0004627902140219
In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge
Management and Information Sharing (KDIR-2013), pages 214-219
ISBN: 978-989-8565-75-4
Copyright
c
 2013 SCITEPRESS (Science and Technology Publications, Lda.)

so-called Extraction, Transform and Load (ETL)
processes. Finally, data is summarized by applying
efficient BI tools such as OLAP.
From a BI point of view, opinion data can be
also multidimensionally modelled and analysed. For
example, the reputation of a product, the most
outstanding features of some product brand, or the
opined aspects can be efficiently computed with
OLAP-like operations (García-Moya et al., 2013a).
The main BI e-commerce patterns we consider in
this project are summarized in Figure 1. Facts such
as sales, offers and opinions account for spatio-
temporal observations of some measure (e.g., units
sold, units offered, number of positive reviews, and
so on), whereas dimensions (labelled with ‘D’)
account for the contexts of such observations.
Dimensions can provide different detail levels
(labelled with ‘L’). In this paper we will mainly
focus on the specification and generation of both
review and opinion facts. Notice that every review
produces two kind of sentiment facts: the global
review assessment about the item (review fact), and
the specific criticisms to the item features/aspects
(opinion facts).
Figure 1: Main BI patterns over e-commerce facts.
In order to cover these patterns, the main
components of i-SLOD data infrastructure are linked
to each other as well as to other external related
LOD datasets. Figure 2 shows the proposed
architecture, where i-SLOD components are placed
within the inner ring. The outer ring contains other
LOD datasets and vocabularies (dotted boxes) that
can be linked to the proposed infrastructure in order
to enrich or perform exploratory BI.
Every i-SLOD component consists of a series of
RDF-triples datasets regarding some of the
perspectives we consider relevant for BI over
sentiment data. As proposed in LOD, links between
datasets are expressed with “
owl:sameAs ” statements.
Links to external datasets like DBpedia play a very
relevant role in this infrastructure since they can
enormously facilitate the migration of existing
review and opinion data. For example, reviews
already containing microdata referring to some
product in DBpedia will be automatically assigned
to the product URI of the corresponding i-SLOD
product dataset.
Figure 2: Main components of I-SLOD, and their relation
to existing LOD vocabulary and data sets.
Regarding the nature of the data to be published in
this infrastructure, we have identified some basic
requirements in order to make published data useful
in a real BI scenario:
 Support classification of sentiment data through
taxonomical relationships.
 Support massive generation of opinion data from
reviews texts.
 Support high distribution of data, providing
optimal partitions w.r.t. to data usage.
 Provide fresh data by migrating as quickly as
possible published reviews.
 Adapt as much as possible existing vocabularies in
e-commerce in order to facilitate the load of data
from different sources.
 Ensure quality and homogeneity of the i-SLOD
datasets, dealing with the multi-lingual issues of
this BI scenario.
3 i-SLOD DATASETS
In this section, we briefly describe the main datasets
that will constitute the i-SLOD data infrastructure
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(inner ring of Figure 2). The main criteria we have
followed to define these datasets are the following:
 Take profit from existing vocabularies and
schemas as much as possible.
 Distribute linked data according to both the
identified BI demands and the fact extraction from
raw texts.
 Keep the inner datasets coherent.
The rest of the section shows the most relevant
aspects of the datasets included in each component.
3.1 Items Component
This component contains the datasets describing
concrete products and services as well as their
manufacturers (e.g., product brand). These datasets
must be kept as simple as possible just providing the
attributes useful for BI tasks. Other attributes and
relationships can be accessed through the links to
externals datasets such as eCl@ss, DBpedia,
ProductDB, FreeBase, etc. For the sake of
simplicity, this component just regards two root
classes: Item and Manufacturer. The schema for the
former is shown in Table 1.
Table 1: Item i-SLOD schema.
Property Description
s:itemID
Unique identifier of the item.
gr:hasManufacturer
URI of the manufacturer.
rdf:label
Item name.
slod:onDomain
Item family.
rdf:type
Type of item (product, service).
For this component we adopt the vocabularies of
Schema.org (s) and GoodRelations (gr). It is worth
mentioning that, although there are several datasets
about products in the LOD cloud, they do not cover
all products and services. In order to perform BI
tasks, this is a serious limitation since an analytical
query requires all data be expressed under the same
schema. This limitation is present in MARL
approach (Westerski and Iglesias, 2011), as opinion
products are arbitrarily linked to either external
datasets or literals. In our case, we propose a
homogeneous schema, which can be further linked
to external datasets.
3.2 Facets Component
This component comprises all the elements subject
to evaluation in the opinions. In this work, the
concept feature is used for denoting concrete
physical parts of an item (e.g., zoom, room, etc.),
whereas the concept aspect is used for abstract
concepts (e.g., design, price, etc.).
Table 2: Facets i-SLOD Schema.
Property Description
slod:facetID
Unique identifier of a facet.
rdf:label
Facet labels.
slod:onDomain
Item family to which it is defined.
rdf:type
Facet type (feature, aspect, etc.).
There are few LOD datasets including facets subject
to opinions. We can find technical specifications
about products like in eCl@ss, but they do not cover
well the features customers usually opine (García-
Moya et al., 2013b). As a consequence, sentiment
analysis approaches aim at extracting these features
directly from text reviews by applying machine
learning techniques (Liu, 2012).
Indeed, one of the i-SLOD goals is to
conceptualize and make public facets that can be
automatically extracted from reviews. For this
purpose, we propose a simple schema (see Table 2)
to which item facets must map to. The main issues
for performing these mappings are: to group together
expressions denoting the same facet, to distinguish
between features and aspects, and to classify
features w.r.t. aspects. Our starting point for
addressing these issues is the statistical approach
presented at (García-Moya et al., 2013b).
3.3 Indicators Component
Sentiment analysis relies on the existence of a set of
words and expressions that indicate some opinion
about a subject. The Indicators component is mainly
based on linguistic resources that allow identifying
facets from review texts as well as sentiments
associated to them.
3.3.1 Opinion Words
Opinion words, also known as sentiment words, are
the most important indicators of sentiments about a
subject. These are words commonly used to express
positive or negative opinions. For example excellent,
amazing, good are positive words whereas bad,
terrible, awful are negative ones. Additionally, there
also exist sentences used for expressing opinions, for
example, cost a pretty penny, cost an arm and a leg
or cost the earth, in this case all are referring to the
indicator concept expensive.
Opinion indicators could be defined as context-
independent or context-dependent (Lu et al., 2011).
An opinion indicator is context-dependent when its
polarity depends on the domain and/or the features it
is modifying (e.g., unexpected for movies (+) and
electronic devices ()). Even within the same
domain, the polarity of an indicator may be different
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depending on the feature. For example, the word
long in digital cameras: “long delay between shots”
() and “long battery life” (+). Another interesting
kind of opinion indicators consists of expressions
that implicitly bring the feature. For example, the
indicator “too expensive” refers to the aspect
“price”.
For the Indicators component we propose two
classes:
slod:Indicator and slod:Polarity. Table 3 shows
the main properties for the indicator class according
to the previous comments.
Table 3: Properties for opinion indicators.
Property Description
slod:indicatorID
Unique identifier of a sentiment.
rdf:label
Sentiment words and sentences.
rdf:type
Type of indicator.
slod:onFacet
Associated facet (implicit/context).
slod:hasPolarity
Polarity associated to the indicator.
Nowadays there exist many sentiment lexicons,
some of them available in LOD. The most popular
ones are SentiWordNet (Esuli and Sebastiani, 2006)
and SenticNet (Cambria et al., 2013), which provide
sentiment-based characterizations for common
words in English. Unfortunately, these lexicons are
of limited use because they are only applicable to
English-written reviews, and they do not take into
account context-based indicators (Lu et al., 2011). It
is worth mentioning that there exist also some web
services like SentiStrength (Thelwall et al., 2010)
that compute polarities from free-texts. This kind of
services could be applied over this dataset to infer
the values of
slod:hasPolarity.
3.3.2 Opinion Shifters
Opinion indicators may not be sufficient to
determine the true or contextual polarity of the
feature. The valence of a polar term may be
modified by one or more words, called contextual
valence shifters. These shifters can be categorized
into several types, some of them are: negations (not,
never, none, etc.), intensifiers (deeply, very, little,
rather, etc.), modal shifters (might, possibly, etc.),
and presuppositions (e.g., lack, neglect, fail, etc.)
There are other kinds of shifters (Polanyi and
Zaenen, 2006), but they are less useful for BI
Table 4: Properties for opinion shifters.
Property Description
slod:shifterID
Unique identifier for the shifter.
slod:change
Change applied to the indicator.
rdf:label
Expresions associated to the shifter.
rdf:type
Type of shifter.
analysis. Table 4 shows the main properties of the
shifter class.
3.4 Reviews Component
Currently, we can find many proposals for
representing review metadata in LOD. One of the
main references is Schema.org, which has been
adopted by Google for rich snippets over reviews.
This vocabulary covers all aspects we need for the
Reviews component, and therefore we have adopted
it without extensions. Table 5 shows some properties
associated to the review class.
Table 5: Properties for review objects.
Property Description
s:reviewrating
Overall assessment (s:rating).
s:itemreviewed
Item reviewed.
s:reviewer
Author of the review.
s:dtreviewed
Publication date of the review.
3.5 Opinion Facts Component
Opinion facts express the associations between
features/aspects to opinion indicators that appear at
the review texts.
Table 6: Opinion facts properties.
Property Description
slod:opinionId
Unique identifier of an opinion fact.
slod:onFacet
Opined facet.
slod:fromReview
Review reference.
slod:onTargetItem
In comparisons, the compared item.
slod:compOperator
In comparisons, the operator being
applied (e.g., better, worst, faster, etc.)
In our approach, an opinion fact is always linked to
the review object from which it was identified.
Consequently, each opinion fact takes the time and
place dimensions from its linked review. Thus, the
schema of an opinion fact can be just expressed with
the feature/aspect and indicator/shifters involved in
the fact. Table 6 summarizes the properties
associated to the opinion fact class.
Another kind of opinion facts regarded in (Liu,
2012) is that of product comparisons. To represent
comparisons, two properties to the opinion fact class
are added:
slod:onTargetItem and slod:comOperator.
Notice that we can combine these properties to
express for example a comparison between two
products w.r.t. some aspect (e.g., “it has better zoom
than camera Y”).
The most similar approach for expressing
opinions in LOD is that of MARL (Westerski and
Iglesias, 2011). The main differences of our
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approach w.r.t MARL are the following ones. In our
approach, opinion facts must be always linked to
datasets within i-SLOD. In this way, we can ensure
coherence and homogeneity of data for BI analysis.
Moreover, our proposal uncouples the opinion fact
from its polarity, which should be inferred from
indicators and shifters. Finally, we do not allow
opinion aggregations, as they will be performed by
the analytical tools (see Section 4.3).
4 i-SLOD POPULATION
This section discusses how to populate the main
components of i-SLOD data infrastructure.
4.1 ELT Processes
Similarly to traditional data warehouses (DW), we
propose to populate the i-SLOD infrastructure by
means of Extraction, Load and Transform (ETL)
processes. These processes will be in charge of
continuously processing published reviews to update
i-SLOD datasets. In this context, each component
presents a different dynamicity degree. For example,
review and opinion facts will grow very quickly,
whereas products, features and indicators will
change more slowly.
Table 7: Proposed i-SLOD ETLs.
Component Operators Dynamicity
Product/Service LOD Linking Low
Feature/Aspects
Sentiment analysis
LOD Linking
Low
Opinion
indicators
Lexica extraction
Sentiment analysis
Low
Review
Microdata
xtraction
LOD Linking
High
Opinion fact
Semantic
Annotation
High
Unlike traditional ETLing, i-SLOD processes deal
with RDF and web data. Table 7 shows the main
ETL operators involved in the i-SLOD components.
As it can be noticed, one critical operator consists of
linking all the loaded data to internal and external
datasets (see Figure 2). Another critical operator
consists of applying sentiment analysis to extract
and rank relevant feature/aspects and indicators to be
included in the corresponding datasets.
4.2 Semantic Annotation
We propose to apply automatic semantic annotation
for extracting opinion facts from raw texts, and
linking data. Semantic annotation consists in
identifying concept mentions in the free-texts in
order to link them to existing knowledge resources.
This technique is gaining popularity within the LOD
community as it allows linking unstructured data to
reference knowledge resources (Mendes et al.,
2011). Unfortunately, current tools are all targeted to
Wikipedia.
In our context, semantic annotation should be
performed with any lexicon that can be extracted
from the i-SLOD datasets (
rdf:label statements).
Particularly, we are interested on identifying
features, indicators and shifters in the review text to
extract opinion facts. An example of opinion fact
extraction is shown in Table 8.
Table 8: Example of opinion facts.
review1: “I don’t like the image and sound of this camera”
(slod:oatom1, slod:fromReview, slod:review1)
(slod:oatom1, slod:onFacet, slod:feature123)
image
(slod:oatom1, slod:withIndicator, slod:indct2)
like
(slod:oatom1, slod:hasShifter, slod:shifter10)
don’t
(slod:oatom2, slod:fromReview, review1)
(slod:oatom2, slod:onFacet, feature231)
sound
(slod:oatom2, slod:withIndicator, slod:indct2)
like
(slod:oatom2, slod:hasShifter, slod:shifter10)
don’t
The work in (García-Moya et al., 2013a) will serve
us as basis to define the tailored semantic annotators
necessary to extract opinion facts.
4.3 BI Analysis in i-SLOD
The i-SLOD infrastructure is meant to hold large
datasets of semi-structured data. Linked data is used
as an integrating tool and provides a new
architectural pattern for mapping and
interconnecting data from a variety of sources. Such
infrastructure should provide the analyst with the
means for executing analytic queries.
BI tools provide a summarized view by
aggregating the data over numerical measures
according to contexts (i.e., dimensions). However,
traditional BI is not suitable for linked data.
Complex queries over the i-SLOD infrastructure
require a data processing model for a cloud
architecture that integrates advanced information
extraction and advanced analysis operations (i.e.,
OLAP operators). Fur such purpose, the datasets in
the inner ring of i-SLOD can be partitioned and
distributed according to the BI demands. For
example, datasets can be partitioned with respect to
domains and time slices. Moreover, functional map-
reduce implementations (Dean and Ghemawat,
2004) can process such distributed partitions and
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parallelize complex analysis operators such as filter,
join and aggregate (Sridhar et al., 2009).
In order to speed-up costly operations within the
inner i-SLOD datasets, additional indexing
mechanisms can be applied. For example, instead of
performing a join between the opinion atom and the
indicator datasets every time a user asks a query
involving such datasets, we can build an index that
associates each opinion atom with its indicators.
More challenging is however, to efficiently perform
BI operations involving external datasets, as we do
not have control over the external sources.
On the other hand, the semantics introduced by
the linked data flavour of the i-SLOD also require
new scalable, distributed reasoning techniques able
to efficiently compute new inferences so that they
can be used in the analysis process.
5 CONCLUSIONS
We have presented i-SLOD, a proposal for a data
infrastructure of open linked sentiment data. Its
purpose is to facilitate the massive analysis of
sentiment data by exploiting the ever-increasing
amount of publicly available open linked data.
The i-SLOD components are designed to
describe all necessary information for opinion
analysis (products/services, features/aspects, and
opinion indicators, reviews and facts), and also to
incorporate the functionality required to perform
massive opinion analysis: the extraction of opinion
facts from text reviews, and the linkage of opinion
data to other datasets, using semantic annotation as a
key enabling technology.
This allows the exploitation of opinion-related
dimensions of analysis that are out of reach for
traditional BI applications, thus allowing the
incorporation of crucial strategic information.
ACKNOWLEDGEMENTS
This work has been partially funded by the
“Ministerio de Economía y Competitividad” with
contract number TIN2011-24147.
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