International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064


Downloads: 118

M.Tech / M.E / PhD Thesis | Computer Science & Engineering | India | Volume 5 Issue 5, May 2016


Keyword Based Emotion Word Ontology Approach for Detecting Emotion Class from Text

Ashish V C, Somashekar R, Dr. Sundeep Kumar K


Abstract: Human Computer interaction is a very powerful and most current area of research because the human world is getting more digitize. This needs the digital systems to imitate the human behavior correctly. Emotion is one aspect of human behavior which plays an important role in human computer interaction, the computer interfaces need to recognize the emotion of the users in order to exhibit a truly intelligent behavior. Human express the emotion in the form facial expression, speech, and writing text. Every day, massive amount of textual data is gathered into internet such as blogs, social media etc. This comprises a challenging style as it is formed with both plaint text and short messaging language. This paper is mainly focused on an overview of emotion detection from text and describes the emotion detection methods. These methods are divided into the following four main categories -based, Lexical Affinity method, learning based, and hybrid based approach. Limitations of these emotion recognition methods are presented in this paper and also, address the text normalization using different handling techniques for both plaint text and short messaging language.


Keywords: Digital systems, Human Behavior, Emotion, Intelligent Behavior, Human Express, plain text and Hybrid Based Approach


Edition: Volume 5 Issue 5, May 2016,


Pages: 1636 - 1639


How to Cite this Article?

Ashish V C, Somashekar R, Dr. Sundeep Kumar K, "Keyword Based Emotion Word Ontology Approach for Detecting Emotion Class from Text", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=NOV163818, Volume 5 Issue 5, May 2016, 1636 - 1639

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