PsycheTagger: using hidden Markov model to annotate English text with semantic tags based on emotive content

Authors:
Liaquat Majeed Sheikh;Summaira Sarfraz;Ahsan Nabi Khan
Affiliations:
Department of Computer Sciences and Department of Humanities, FAST, National University of Computer and Emerging Sciences, Lahore, Pakistan;Department of Computer Sciences and Department of Humanities, FAST, National University of Computer and Emerging Sciences, Lahore, Pakistan;Department of Computer Sciences and Department of Humanities, FAST, National University of Computer and Emerging Sciences, Lahore, Pakistan
Venue:
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Year:
2012

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Abstract

The human elements of personality working behind the creation of a write-up play an important part in determining the final dominant mood of a text. This paper presents a tool, PsycheTagger, which extracts the emotive content of a text in English Language in its context and tags each open-class word of the text with one of the predefined psyche categories that represent the emotive content. Working in the lines of statistical Parts-of-Speech Taggers, this tool is an example of a semantic tagger. The tagger self-ranks its choices with a probabilistic score, calculated using Viterbi algorithm run on a Hidden Markov Model of the psyche categories. The results of the tagging exercise are critically evaluated on the Likert scale. These results strongly justify the validity and determine high accuracy of tagging using the probabilistic parser.