Mood and Vulnerability Prediction through Natural Language Processing
Debabrata Datta1, Srijita Majumdar2, Olie Sen3, Aparna Sen4
1Debabrata Datta*, Department of Computer Science, St. Xavier‟s College(Autonomous), Kolkata, India.
2Srijita Majumdar, Department of Computer Science, St. Xavier‟s College(Autonomous), Kolkata, India.
3Olie Sen, Sprinriver Technology Private Limited, Kolkata, India.
4Aparna Sen, XCD HR Private Limited, Kolkata, India.
Manuscript received on November 13, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 1335-1345 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6158129219/2019©BEIESP | DOI: 10.35940/ijitee.B6158.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Analyzing various phases of mood using the verbal form of writing can serve as advancement in the field of psychology. The research work highlighted in this paper focuses on the use of sentiment analysis to predict the emotional state and vulnerability of written statements, as per the most generic perceptions, in the English language, with the help of an algorithm. The text pre-processing step discussed in this work involves cultivating and analyzing each word of user input, analyzing their literal and emotional essences to sum up the mood inclination of the statements and other parts-of-speech, to determine the specific mood and the vulnerability of the writing itself. The vulnerability level of the document is also determined, in order to extent out the purpose towards medical treatments where a vulnerable mindset, suffering from mental illness, depression, perceives the capability to inflict harm upon oneself or others can be given proper help and counseling.
Keywords: Mood Inclination, Text Mining, Natural Language Processing, Connotation Analysis, Porter Stemmer Algorithm.
Scope of the Article: Natural Language Processing