IEEE Style Citation: P. Rani, M.V. Jagannatha Reddy, K.S.M.V. Kumar, Sreedhar S.B., “Detecting Human Emovere through Data Mining,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.64-69, 2020.
MLA Style Citation: P. Rani, M.V. Jagannatha Reddy, K.S.M.V. Kumar, Sreedhar S.B. "Detecting Human Emovere through Data Mining." International Journal of Computer Sciences and Engineering 8.1 (2020): 64-69.
APA Style Citation: P. Rani, M.V. Jagannatha Reddy, K.S.M.V. Kumar, Sreedhar S.B., (2020). Detecting Human Emovere through Data Mining. International Journal of Computer Sciences and Engineering, 8(1), 64-69.
BibTex Style Citation:
@article{Rani_2020,
author = {P. Rani, M.V. Jagannatha Reddy, K.S.M.V. Kumar, Sreedhar S.B.},
title = {Detecting Human Emovere through Data Mining},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2020},
volume = {8},
Issue = {1},
month = {1},
year = {2020},
issn = {2347-2693},
pages = {64-69},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4997},
doi = {https://doi.org/10.26438/ijcse/v8i1.6469}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i1.6469}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4997
TI - Detecting Human Emovere through Data Mining
T2 - International Journal of Computer Sciences and Engineering
AU - P. Rani, M.V. Jagannatha Reddy, K.S.M.V. Kumar, Sreedhar S.B.
PY - 2020
DA - 2020/01/31
PB - IJCSE, Indore, INDIA
SP - 64-69
IS - 1
VL - 8
SN - 2347-2693
ER -
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Abstract
With the growth of the Internet community, textual data has proven to be the main tool of communication in human-human interaction. This communication is constantly evolving towards the goal of making it as human and real as possible. One way of humanizing such interaction is to provide a framework that can recognize the emotions present in the communication or the emotions of the involved users in order to enrich user experience. The use of social networking sites is one of the approaches for putting views of user. Proposed emotion detector system takes a text document or audio and the emotion word ontology as inputs and produces the scores of six emotion classes (i.e. happy, sad, fear, surprise, anger and disgust) as the output; for twitter data as input the extracted tweets are categorized in to positive, negative and neutral tweets.
[1] Protégétool,www.protege.stanford.edu/NicuSebea, Ira Cohenb, Theo Geversa, and Thomas S. Huangc“Multimodal Approaches for Emotion Recognition: A Survey”, USA
[2] http://sail.usc.edu/~kazemzad/emotion_in_text_cgi/DAL_app/index.php?overall=bad&submit_evaluation=Submit+Query”.
[3] www.wikipedia.org/Shiv NareshShivhare, SarithaKhethawat, “Emotion Detection from Text”, Second International Conference on Computer Science, Engineering and Applications (CCSEA-2012), May 26-27, Delhi, India, ISBN: 978-1-921987-03-8. 2012.
[4] R Cowie, E. Douglas-Cowie “Emotion recognition in Human-Computer Interaction” in IEEE Signal Processing Magazine volume 18(1), 2001.
[5] Chun-Chieh Liu, Ting-Hao Yang, Chang-Tai Hsieh, Von-WunSoo, “Towards Text-based Emotion Detection: A Survey and Possible Improvements”, in International Conference on Information Management and Engineering, 2009.
[6] Parrott, W.G, “Emotions in Social Psychology,” in Psychology Press, Philadelphia 2001.
[7] Haxby J V, Hoffman and Gobbini M I 2002 Human neural system for face recognition and social communication Biol. Pyschiatry51pp 59-67.
[8] Ekman P 2015 an Argument for Basic Emotion International Journal of Cognition and Emotion 6.
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