Contextualized and Enriched Psycholinguistic Commonsense Ontology
Haytham Mohtasseb, Amr Ahmed, Amjad AlTadmri and David Cobham
School of Computer Science, University of Lincoln, Brayford Pool, Lincoln, U.K.
Commonsense knowledge base, Semantic network, Ontology development, Psycholinguistic, Text classifica-
PsychoNet 1 has demonstrated the feasibility of integrating psycholinguistic taxonomy, represented in LIWC,
and its semantic textual representation in the form of commonsense ontology, represented in ConceptNet.
However, various limitations exist in PsychoNet 1, including the lack of concluding context of the concept
annotation. In this paper, we address most of those limitations and introduce a new enhanced and enriched
version, PsychoNet 2. PsychoNet 2 utilizes WordNet, in addition to LIWC and ConceptNet, to produce an in-
tegrated contextualized psycholinguistic ontology. The first and the main contribution is that, in PsychoNet 2,
each concept is annotated by the potential (most representative) contextual psycholinguistic categories, rather
than all applicable categories. The second contribution is the enrichment of LIWC through utilizing WordNet.
This in fact produced an enriched version of LIWC that may also be used independently in other applications.
This has contributed to substantial enrichment of PsychoNet 2 as it facilitated including additional number of
concepts that were not included in PsychoNet 1 due to lack of corresponding words in the original LIWC. A
sample application of text classification, for a mood prediction task, is presented to demonstrate the introduced
enhancements. The results confirm the improved performance of the new PsychoNet 2 against PsychoNet 1.
The ontology engineering community is increasingly
convening to develop more work towards integrat-
ing ontologies so that they can share and reuse each
other’s knowledge (Noy and Hafner, 1997). Psy-
choNet 1 (Mohtasseb and Ahmed, 2010b) introduced
a novel commonsense knowledgebase that forms the
link between the psycholinguistic and its semantic
textual representation. It allows the researcher to use
one coherent knowledgebase that has the power of se-
mantic commonsense and psycholinguistic taxonomy.
There are many types of tagging and integration
(more details in Section 2), but this study presents
the benefits of integrating LIWC, ConceptNet, and
WordNet for a wide range of applications. This pa-
per develops ConceptNet, a commonsense ontology
(Liu and Singh, 2004), by adding a psycholinguistic
layer, utilizing LIWC (Pennebaker et al., 2001), en-
riched by the lexical semantic network namely Word-
Net (Miller, 1995). Furthermore, in PsychoNet 2,
only the common highly rated annotations are kept
as they represent the context of the concept.
The rest of the paper is organized as follows. Sec-
tion 2 reviews the recent work related to our domain.
Section 3 presents PsychoNet 2 including the enrich-
ment and the contextualization processes. Section 4
shows the application of PsychoNet 2 in mood classi-
fication and its results. Finally, the paper is concluded
in Section 5.
This section presents an overview of the related work
and the existing development in the same area includ-
ing LIWC, ConceptNet, WordNet, and PsychoNet 1.
Linguistic Inquiry Words Count (LIWC) (Pen-
nebaker et al., 2001) has been built by classifying a
nominated set of 2000 words (and word stems) into
several dozens of psycho categories, based on the
judgment of a group of linguistic experts. The cate-
gories include positive and negative emotional words
, functional words (pronouns, articles, prepositions),
health and biology categories, and other contextual
categories (e.g., sport, family, religion, death). LIWC
had been used successfully in numerous text analy-
ses tasks for analyzing the emotions of users in blog
Mohtasseb H., Ahmed A., AlTadmri A. and Cobham D..
PSYCHONET 2 - Contextualized and Enriched Psycholinguistic Commonsense Ontology.
DOI: 10.5220/0003621403390343
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 339-343
ISBN: 978-989-8425-80-5
2011 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1: PsychoNet 2 Building Framework.
text (Gill et al., 2008; Hancock et al., 2008; Hancock
et al., 2007), identifying the gender of bloggers (Now-
son and Oberlander, 2006), recognizing the personal-
ity (Gill, 2003; Mairesse et al., 2007), studying the de-
mographic differentiations across the styles of blog-
gers (Mohtasseb and Ahmed, 2010a), and in author-
ship identification (Mohtasseb and Ahmed, 2009a;
Mohtasseb and Ahmed, 2009b). However, all of these
tasks have been applied on the word level rather than
the concept level, which is available in PsychoNet 1.
The ConceptNet knowledgebase is a semantic
network encompasses the spatial, physical, social,
temporal aspects of everyday life (Liu and Singh,
2004). ConceptNet is generated automatically from
the 700,000 sentences of the Open Mind Common
Sense corpus
. ConceptNet is currently considered
to be the largest commonsense semantic network
containing over 250,000 nodes. Nodes are semi-
structured English fragments, interrelated by an on-
tology of twenty semantic relations (predicates). Con-
ceptNet is very useful in describing real life scenes
which makes it a good candidate to be integrated with
LIWC that will add the psycholinguistic dimension.
WordNet is a large lexical database of English
(Miller, 1995). Nouns, verbs, adjectives and adverbs
are grouped into sets of cognitive synonyms (synsets).
It is a very rich domain-independent knowledgebase
of lexical units that consist of various forms of syn-
onyms. WordNet is effective for studying the rela-
tionships within similar words in terms of meaning,
generalization or specialization.
On the other hand, PsychoNet 1 introduced the
first development of ConceptNet towards psycholin-
guistic direction, utilizing LIWC. It has been built by
a fully automated engine that performs lexical anal-
ysis on concepts and extracts the corresponding psy-
cholinguistic categories. It allows the researcher to
use one coherent knowledgebase that has the power of
semantic commonsense and psycholinguistic taxon-
1 push/OMCS-Research.html
omy. Moreover, PsychoNet 1 simplified applying text
classification tasks in ConceptNet and allows filtering
the huge concept graphs based on a key category for
a specific application. PsychoNet 2 introduces further
improvement on PsychoNet 1 as being explained in
the next section.
3 PsychoNet 2
In PsychoNet 1 (Mohtasseb and Ahmed, 2010b), each
node is a concept associated with a psychometric field
that contains the psycholinguistic categories (annota-
tions) and their relevance degree. In PsychoNet 2,
many limitations have been addressed including miss-
ing concepts and contextualization, and more sub-
stantial improvements are introduced through the ad-
dition of two new stages as depicted in Figure 1.
The first stage, Enrichment, utilizes WordNet to deal
with those concepts, existing in ConceptNet, that do
not have matching LIWC annotations. The resulting
synonym sets, for the original component words, are
then annotated using LIWC. This is explained in de-
tail in Section 3.1. Section 3.2 presents the second
stage, Contextualization, that starts by selecting the
synonym sets that share the same set of annotations.
Then, it deduces the high ranked annotations that po-
tentially represent the context of the concept. The fol-
lowing subsections explain the two new stages; En-
richment and Contextualization, respectively.
3.1 Enrichment
Through our analysis of PsychoNet 1, it has been
found that there were 21498 concepts that have not
been included. Moreover, the analysis showed that
31863 words, which belong to the commonsense con-
cepts, do not have matching LIWC categories. To
address this and try to annotate and include most
concepts, we had to develop a way to enrich LIWC
to include those missing words and their variations.
Therefore, WordNet is utilized here to expand and en-
rich the contents of LIWC based on the commonsense
words of ConceptNet, as explained below.
Assume that W = {w
, w
, . . . , w
} is the set of
commonsense words that do not have LIWC anno-
tations. For each word w
W , all synsets (synonym
sets) {S
, S
, . . . , S
}, of this word, are extracted using
WordNet. Hence, S
= {s
, s
, . . . , s
} represents one
of the synsets where s
is a synonym for w
within the
context of the synset S
. A
= {a
, a
, . . . , a
} is the
list of LIWC annotation of S
if there were cross joint
annotations across all s
. Then, the set of final LIWC
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
Table 1: Snapshot of the result showing the added common-
sense words to LIWC using WordNet.
Word Synset Annotation
earth world,globe Relativity,Space
earth ground Relativity,Space
absorbing engross,engage,
live alive Biological,
live exist,survive,
awake alert,alive Biological,
cereal food grain Ingestion, Bio-
newspaper paper Work
audience hearing Perceptual, So-
calculate account Money
gift endow,empower,
gift endowment,talent Affective,
crime law-breaking,
annotations A
of w
is produced by the union of the
annotations of synsets A
= {A
··· A
According to the approach described above, if a
word w
has a non-empty annotation set A
, then w
added to the corresponding list of words of its relevant
psycholinguistic categories. In addition, the annota-
tion list A
will contribute to the concept annotation
where w
originated from (Section 3.2).
Table 1 shows a snapshot of the resulting new
words along with the assigned annotations. As a re-
sult of the above enrichment stage, 7772 words have
been added to LIWC, 8663 new concepts have been
included in PsychoNet 2, and 56615 concepts have
been enriched with extra annotations. This is a mutual
benefit for those who want to use LIWC alone, with
this enriched version, and for those who still need to
use the full PsychoNet 2 knowledgebase.
It is worth mentioning that the number of anno-
tation sets A
might not be equal to the number of
synsets. This is because in some cases there might be
a synset S
that has no representative psycholinguis-
tic annotation (i.e. has an empty A
annotation set).
Therefore, we can see in Table 1 that the enrichment
process provides two matching synsets with different
sets of annotation for the word live, however, it only
provides one annotation for the word newspaper.
3.2 Contextualization
PsychoNet 1 associates each concept with a list of
psycholinguistic annotations and its corresponding
frequencies. This is due to the existence of com-
mon annotations across the words of the concept. Al-
though there could be multiple annotations for the
same word, it should only select the annotations that
are related to the context. In PsychoNet 2, it is
intended to select the psycholinguistic annotations
based on the context of the representing words. This
will maintain only psycholinguistic annotations (cat-
egories) which suit the context of the concept. Table
2 shows an example of annotations results before and
after contextualization.
We can see that the concept “a scream of free-
dom” has conflicting annotations; Neg.Emotion and
Pos.Emotion, resulting from its component words.
Moreover, it located Hearing which is related to one
of the words, but it is outside of the context for this
concept. The proposed algorithm ended up with Af-
fective annotation which is more representative of the
context of that concept. The same can be seen in the
concept “The best way to commit a crime”. Similarly,
the concept “coffee shop” has Leisure as the context
annotation. Many other concepts have not been in-
cluded before in PsychoNet 1, such as “hit ball” and
“zip code”. But now, in PsychoNet 2, they are in-
cluded and annotated, thanks to the enrichment of the
LIWC by utilizing the WordNet (Section 3.1).
This section presents a sample text classification ap-
plication using PsychoNet 2. The contribution lies in
accuracy improvement achieved using PsychoNet 2
compared to PsychoNet 1 and LIWC respectively. We
utilized the same mood experiment framework and
corpus presented in (Mohtasseb and Ahmed, 2010b)
for building a classification model distinguishing be-
tween moods using LIWC and PsychoNet, for both
versions, respectively. The difference between the
two experiments derives from creating the learning
vectors either by using LIWC to extract the features
from words, or by applying psycholinguistic-index
function (Mohtasseb and Ahmed, 2010b) over the ex-
tracted concepts. For each mood, the F-Measure value
of the classification result is calculated. Results pre-
sented in table 3 shows that PsychoNet 2 outperforms
both LIWC and PsychoNet 1 in all moods. The next
section shows a more detailed discussion of the re-
PSYCHONET 2 - Contextualized and Enriched Psycholinguistic Commonsense Ontology
Table 2: Snapshot of the result showing the previous and new annotations.
Concept Previous Annotations New Annotations
a scream of freedom Affective,Hearing,Perceptual,
coffee shop Ingestion,Biological,Leisure,Money Leisure
swimming pool Relativity,Motion,Leisure Leisure
The best way to commit a
hit balls Nil Leisure
zip code Nil Relativity,Space
Table 3: Mood classification results using F-Measure.
Mood LIWC PNet 1 PNet 2
amused 0.40 0.56 0.59
cheerful 0.39 0.48 0.49
busy 0.40 0.56 0.67
happy 0.42 0.56 0.61
calm 0.33 0.44 0.48
content 0.28 0.42 0.52
creative 0.36 0.24 0.41*
bored 0.39 0.50 0.53
contemplative 0.30 0.45 0.58
exhausted 0.44 0.30 0.48*
4.1 Discussion
LIWC has been used successfully in various classifi-
cation/identification tasks where the target classes are
objective facts, such as Gender, Age, or Authorship
Identification. However, the results of using LIWC
in mood classification are poor and not promising as
depicted in Table 3. This is mainly because the tar-
get class (mood) is subjective rather than objective,
and may not be accurately provided by the user. It
is usual that a user tags a number of posts with dif-
ferent moods even where the contents are, to some
extent, similar. Hence, this task is challenging and
LIWC features alone are not enough to fulfill it. Pre-
vious studies in mood prediction confirm this diffi-
culty as they utilized various types of features in order
to achieve reasonable results (Mishne, 2005; Leshed,
As demonstrated in the experiment above, using
PsychoNet 2 improved the result of mood classifica-
tion compared to both LIWC and PsychoNet 1. Psy-
choNet 1 enhanced the result for some moods and im-
proved accuracy to above 50% for others. However,
PsychoNet 2 made enhancement in all moods and im-
proved the accuracy to over 60%, for some moods.
Furthermore, we can see that LIWC outperformed
PsychoNet 1 in some moods (annotated with stars in
Table 3). But the results confirm that PsychoNet-2
outperformed LIWC in all moods.
In this paper, we presented PsychoNet 2, a substan-
tially contextualized and enriched psycholinguistic
commonsense ontology. The overall main contribu-
tion is the creation of one cohesive semantic network
and ontology based on the integration of three im-
portant text analysis resources namely: ConceptNet,
LIWC, and WordNet. This addresses various limita-
tions of PsychoNet 1, including contextualization and
missing concepts. The first contribution, in this paper,
is the contextual annotation of nodes. This contex-
tualization annotates each node with the most repre-
sentative contextual psycholinguistic categories. The
second contribution is the enrichment of the LIWC,
through utilizing the WordNet. This enrichment pro-
cess added 7772 new words to the LIWC lexicon
and associated them with the relevant psycholinguis-
tic categories. Consequently, this enrichment led to
the enrichment of the PsychoNet 2 by additional 8663
concepts, which were missing in PsychoNet 1, and
improved the annotation of another 56615 concepts.
PsychoNet 2 can be used in many applications in text
engineering. We present here one application in mood
classification. The results confirm the validity of Psy-
choNet 2 and showed the improvements experienced
in all moods compared to LIWC and PsychoNet 1.
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