Sensing the Mood on Crimes Against Women by Exploring Social Media Using Dimensional Model
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https://doi.org/10.14419/ijet.v7i2.20.17374 -
Arousal, mood, social media, twitter, valence. -
Abstract
Nowadays social media plays a vital role in sharing the information, sharing the views on a particular topic, expressing the sentiments and opinions. Mood is a sub form of sentiment analysis and opinion mining which generally describes the state of mind of a person whether happy, sad, fear and anger etc. Twitter is one of the most popular sources of public opinions. Sensing of mood is generally required to analyze the impact of marketing campaigns, launching of new products etc. This paper focuses on sensing the mood on crimes against women in social media using the quantitative measures of valence and arousal. This paper gives a comparative analysis on mood sensing among different time periods over the same topic.
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References
[1] Sinha A & Singha P, “CRIME AGAINST WOMEN IN INDIA: TREND AND REGIONAL VARIATIONâ€, International Journal, Vol.3, No.10,(2015), pp.1403-1406.
[2] Liu B, Hu M & Cheng J, “Opinion observer: analyzing and comparing opinions on the webâ€, Proceedings of the 14th international conference on World Wide Web, (2005), pp.342-351.
[3] Bradley MM & Lang PJ, “Affective norms for English words (ANEW): Instruction manual and affective ratingsâ€, Technical report C-1, the center for research in psychophysiology, University of Florida, (1999).
[4] PreoÅ£iuc-Pietro D, Schwartz HA, Park G, Eichstaedt J, Kern M, Ungar L & Shulman E, “Modelling valence and arousal in facebook postsâ€, Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, (2016), pp.9-15.
[5] Inkpen D, Keshtkar F & Ghazi D, “Analysis and generation of emotion in textsâ€, KEPT, (2009), pp.3-13.
[6] Ghazi D, Inkpen D & Szpakowicz S, “Hierarchical versus flat classification of emotions in textâ€, Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, (2010), pp.140-146.
[7] Keshtkar F & Inkpen D, “Using sentiment orientation features for mood classification in blogsâ€, International Conference on Natural Language Processing and Knowledge Engineering, (2009), pp.1-6.
[8] Paltoglou G & Thelwall M, “Seeing stars of valence and arousal in blog postsâ€, IEEE Transactions on Affective Computing, Vol.4, No.1,(2013), pp.116-123.
[9] Mishne G, “Experiments with mood classification in blog postsâ€, Proceedings of ACM SIGIR workshop on stylistic analysis of text for information access, Vol.19, (2005), pp.321-327.
[10] Kamps, J., Marx, M., Mokken, R.J. and De Rijke, M., 2004, May. Using WordNet to Measure Semantic Orientations of Adjectives. In LREC (Vol. 4, pp. 1115-1118).
[11] De Choudhury M, Gamon M & Counts S, “Happy, nervous or surprised? classification of human affective states in social mediaâ€, Sixth International AAAI Conference on Weblogs and Social Media, (2012).
[12] De Choudhury M & Counts S, “The nature of emotional expression in social media: measurement, inference and utilityâ€, Human Computer Interaction Consortium (HCIC), (2012).
[13] Kuppens P, Tuerlinckx F, Russell JA & Barrett LF, “The relation between valence and arousal in subjective experienceâ€, Psychological Bulletin, Vol.139, No.4,(2013).
[14] Dodds PS & Danforth CM, “Measuring the happiness of large-scale written expression: Songs, blogs, and presidentsâ€, Journal of happiness studies, Vol.11, No.4, (2010), pp.441-456.
[15] Kim SM & Hovy E, “Determining the sentiment of opinionsâ€, Proceedings of the 20th international conference on Computational Linguistics, (2004).
[16] Mac Kim S, Recognising emotions and sentiments in text, University of Sydney, (2011).
[17] Nguyen T, “Mood patterns and affective lexicon access in weblogsâ€, Proceedings of the ACL Student Research Workshop, (0210), pp.43-48.
[18] Nguyen T, Phung D, Adams B, Tran T & Venkatesh S “Classification and pattern discovery of mood in weblogsâ€, Pacific-Asia Conference on Knowledge Discovery and Data Mining, (2010), pp.283-290.
[19] Lampoltshammer TJ, Kounadi O, Sitko I & Hawelka B, “Sensing the public's reaction to crime news using the ‘Links Correspondence Method’â€, Applied geography, Vol.52, (2014), pp.57-66.
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How to Cite
Anto Arockia Rosaline, R., & Parvathi, R. (2018). Sensing the Mood on Crimes Against Women by Exploring Social Media Using Dimensional Model. International Journal of Engineering & Technology, 7(2.20), 384-388. https://doi.org/10.14419/ijet.v7i2.20.17374Received date: 2018-08-11
Accepted date: 2018-08-11