Psychonet 2: Contextualized and enriched psycholinguistic commonsense ontology

H. Mohtasseb, A. Ahmed, A. AlTadmri, D. Cobham

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

1 Citation (Scopus)


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 integrated 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 utilizingWordNet. This in fact produced an enriched version of LIWC that may also be used independently in other applications. This has contributed to sub stantial 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.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Knowledge Engineering and Ontology Development
Publication statusPublished - 2011
EventInternational Conference on Knowledge Engineering and Ontology Development: KEOD 2011 - Paris, France
Duration: 26 Oct 201129 Oct 2011


ConferenceInternational Conference on Knowledge Engineering and Ontology Development

Research Centres

  • Data and Complex Systems Research Centre
  • Data Science STEM Research Centre


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