PSYCHONET 2

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.

Keywords:

Commonsense knowledge base, Semantic network, Ontology development, Psycholinguistic, Text classiﬁca-

tion.

Abstract:

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.

1 INTRODUCTION

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.

2 BACKGROUND

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

339

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.)

&RQFHSW1HW

:RUG1HW

/,:&

(QULFKPHQW

&RQFHSWV&!

6\QVHWV6!

$QQRWDWLRQV$! $QQRWDWLRQ &RQWH[WXDOL]DWLRQ

&6!

&6$!

&$!

3V\FKR1HW

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-

http://web.media.mit.edu/ 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

340

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,

occupy

Affective,

Pos.Emotion

live alive Biological,

Health,Death

live exist,survive,

subsist

Achievement

awake alert,alive Biological,

Health

cereal food grain Ingestion, Bio-

logical

newspaper paper Work

audience hearing Perceptual, So-

cial,Hearing

calculate account Money

gift endow,empower,

invest,endue

Money

gift endowment,talent Affective,

Pos.Emotion

crime law-breaking,

offense

Neg.Emotion,

Affective,Anger

annotations A

of w

is produced by the union of the

annotations of synsets A

= {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).

4 APPLICATION: MOOD

CLASSIFICATION

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-

sults.

PSYCHONET 2 - Contextualized and Enriched Psycholinguistic Commonsense Ontology

341

Table 2: Snapshot of the result showing the previous and new annotations.

Concept Previous Annotations New Annotations

a scream of freedom Affective,Hearing,Perceptual,

Neg.Emotion,Pos.Emotion

Affective

coffee shop Ingestion,Biological,Leisure,Money Leisure

swimming pool Relativity,Motion,Leisure Leisure

The best way to commit a

crime

Quantiﬁer,Affective,Cognitive,Anger,

Certainty,Achievement,Relativity,

Neg.Emotion,Pos.Emotion

Relativity,Cognitive,

Certainty,Affective

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,

2006).

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.

5 CONCLUSIONS

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.

REFERENCES

Gill, A. (2003). Personality and language: The projection

and perception of personality in computer-mediated

communication.

Gill, A. J., French, R. M., Gergle, D., and Oberlander, J.

(2008). The language of emotion in short blog texts.

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

342

In Proceedings of the ACM 2008 conference on Com-

puter supported cooperative work, pages 299–302.

ACM New York, NY, USA.

Hancock, J. T., Gee, K., Ciaccio, K., and Lin, J. M. H.

(2008). I’m sad you’re sad: emotional contagion in

cmc. In Proceedings of the ACM 2008 conference

on Computer supported cooperative work, pages 295–

298. ACM New York, NY, USA.

Hancock, J. T., Landrigan, C., and Silver, C. (2007). Ex-

pressing emotion in text-based communication. In

Proceedings of the SIGCHI conference on Human fac-

tors in computing systems, pages 929–932. ACM New

York, NY, USA.

Leshed, G. (2006). Understanding how bloggers feel:

recognizing affect in blog posts. In Conference on

Human Factors in Computing Systems, pages 1019–

1024. ACM New York, NY, USA.

Liu, H. and Singh, P. (2004). Conceptneta practical com-

monsense reasoning tool-kit. BT Technology Journal,

22(4):211–226.

Mairesse, F., Walker, M. A., Mehl, M. R., and Moore, R. K.

(2007). Using linguistic cues for the automatic recog-

nition of personality in conversation and text. Journal

of Artiﬁcial Intelligence Research, 30:457–500.

Miller, G. A. (1995). Wordnet: a lexical database for en-

glish. Communications of the ACM, 38(11):39–41.

Mishne, G. (2005). Experiments with mood classiﬁcation

in blog posts. In Proceedings of ACM SIGIR 2005

Workshop on Stylistic Analysis of Text for Information

Access.

Mohtasseb, H. and Ahmed, A. (2009a). Mining online di-

aries for blogger identiﬁcation. In The 2009 Interna-

tional Conference of Data Mining and Knowledge En-

gineering (ICDMKE’09).

Mohtasseb, H. and Ahmed, A. (2009b). More blog-

ging features for author identiﬁcation. In The 2009

International Conference on Knowledge Discovery

(ICKD’09).

Mohtasseb, H. and Ahmed, A. (2010a). The Affects of De-

mographics Differentiations on Authorship Identiﬁca-

tion, pages 409–417. Springer.

Mohtasseb, H. and Ahmed, A. (2010b). Psychonet: a psy-

cholinguistc commonsense ontology. In The Inter-

national Conference on Knowledge Engineering and

Ontology Development KEOD, pages 159–164.

Nowson, S. and Oberlander, J. (2006). The identity of blog-

gers: Openness and gender in personal weblogs. In

Proceedings of the AAAI Spring Symposia on Compu-

tational Approaches to Analyzing Weblogs.

Noy, N. F. and Hafner, C. D. (1997). The state of the art in

ontology design. AI magazine, 18(3):53–74.

Pennebaker, J. W., Francis, M. E., and Booth, R. J. (2001).

Linguistic inquiry and word count: Liwc 2001. Mah-

way : Lawrence Erlbaum Associates.

PSYCHONET 2 - Contextualized and Enriched Psycholinguistic Commonsense Ontology

343