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Sentiment Analysis to Improve Emotional Health of User

by Pranav Rane, Kashyap Bhansali, Sindhu Nair
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 1
Year of Publication: 2015
Authors: Pranav Rane, Kashyap Bhansali, Sindhu Nair
10.5120/21191-3844

Pranav Rane, Kashyap Bhansali, Sindhu Nair . Sentiment Analysis to Improve Emotional Health of User. International Journal of Computer Applications. 120, 1 ( June 2015), 21-24. DOI=10.5120/21191-3844

@article{ 10.5120/21191-3844,
author = { Pranav Rane, Kashyap Bhansali, Sindhu Nair },
title = { Sentiment Analysis to Improve Emotional Health of User },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 1 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 21-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number1/21191-3844/ },
doi = { 10.5120/21191-3844 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:05:05.877347+05:30
%A Pranav Rane
%A Kashyap Bhansali
%A Sindhu Nair
%T Sentiment Analysis to Improve Emotional Health of User
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 1
%P 21-24
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The primary idea of the project is to change the mind-set of the user by providing various kinds of media or activities with the help of Sentiment Analysis. The user's current mind-set is detected using sentiment analysis and the negative emotion is countered by showing appropriate media to the user. The input will be the emotional state of user in words and will be used to determine the exact emotion. This will allow us to show tailor-made results that are capable of overpowering the currently negative mind-set. The media response will change from user to user and will vary at different times of the day. A combination of fine sentiment analysis and intelligent profile management will be used to accomplish the results. A model is designed to learn from the user's behavioral traits. Thus the goal of transforming the user's mental state to achieve longer positive mentality shall be achieved.

References
  1. Soumaya Chaffar and Diana Inkpen, "Using a Heterogeneous dataset for Emotional Analysis In Text," Advances in Artificial Intelligence, Lecture Notes in Computer Science Volume 6657, 2011
  2. Vivek Narayanan, Ishan Arora and Arjun Bhatia, "Fast and accurate sentiment classification using an enhanced Naive Bayes model," Intelligent Data Engineering and Automated Learning – IDEAL 2013, Lecture Notes in Computer Science Volume 8206, 2013
  3. Bo Pang, Lillian Lee and Shivakumar Vaithyanathan, "Thumbs up? Sentiment classification using machine learning techniques," Proceedings of EMNLP, 2002
  4. Andrew B. Armstrong, "Basis for Ratio Advice", "Altering positive/negative interaction ratios in relationships of mothers and young children", Utah, Proquest, Umi Dissertation Publishing
  5. Carlo Strapparava and Rada Mihalcea, "Annotating and Identifying Emotions in Text", ACM SAC, 2008
  6. Alena Neviarouskaya, Helmut Prendinger and Mitsuru Ishizuka, "Recognition of Fine-Grained Emotions from Text: An Approach Based on the Compositionality Principle", Modeling Machine Emotions for Realizing Intelligence, 2009
  7. Martin D. Sykora, Thomas W. Jackson, Ann O'Brien and Suzanne Elayan, "Emotive Ontology: Extracting Fine-Grained Emotions From Terse, Informal Messages", IADIS International Journal on Computer Science and Information Systems, 2013, Vol. 8, No. 2
  8. Saima Aman and Stan Szpakowicz, "Identifying Expressions of Emotion in Text", Text, Speech and Dialogue, 2007
  9. Svetlana Kiritchenko, Xiaodan Zhu Xiaodan and Saif M. Mohammad, "Sentiment Analysis of Short Informal Texts", 2014, Journal of Artificial Intelligence Research", Vol. 50
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Machine Learning Naïve Bayes

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