An approach to analyze suicidal tendency in blogs and tweets using Sentiment Analysis

Shrija Madhu¹

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.4 , pp.34-36, Aug-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i4.3436

Online published on Aug 31, 2018

Copyright © Shrija Madhu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

739 Views    448 Downloads    132 Downloads

Abstract :
Sentiment Analysis(SA) or Opinion Mining is done to find the opinion of the users and customers to review and analyse their opinions on various products and services. It is one of the major tasks in business these days for knowing demands of customers. In this paper a sentiment analysis method for analyzing the suicidal tendencies in blogs and tweets is proposed. The proposed method uses concepts like Bag of words ,Part of Speech and Natural Language Processing for analzing the text.In life sometimes such a situation arises where the person finds himself trapped and suicide only seems to be the ultimate respite from all problems. Such a person may not share his mental condition verbally with anyone but may share it through tweets and messages. This paper is an approach to reach out a helping hand to such people by analyzing such tendencies in their messages. The proposed method uses Python Language module Textblob for performing different analysis task on the text.

Key-Words / Index Term :
Sentiment Analysis; Suicide; Textblob; blogs;tweets

References :
[1]. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu , K., & Kuksa,P.“Natural language processing (almost) from scratch”. The Journal of Machine Learning Research, 12, 2493–2537, 2011.
[2]. Gimpel, K., Schneider, N., O’Connor, B., Das, D., Mills, D., Eisenstein, J., etal. “Part-of-speech tagging for twitter: Annotation, features, and experiments”. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies: Short papers - volume 2 HLT ’11,pp. 42–47,2011
[3]. Pak, A., & Paroubek, P. “Twitter as a corpus for sentiment analysis and opinion mining”. In LREc: vol. 10,pp. 1320–1326,2010
[4]. Pang, B., Lee, L., & Vaithyanathan, S. “Thumbs up?: sentiment classification using machine learning techniques”. In Proceedings of the ACL-02 conference on
empirical methods in natural language processing-Volume 10,pp. 79–86,2002
[5]. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. “Lexicon-based methods for sentiment analysis”. Computational linguistics, 37, 267–307,2011.
[6]. Xia, R., & Zong, C. “Exploring the use of word relation features for sentiment classification”. In Proceedings of the 23rd international conference on computational linguistics: Posters COLING ’10,pp. 1336–1344,2010
[7]. Kharde,A.V., & Sonawane,S.S. “Sentiment Analysis of Twitter Data: A Survey of Techniques”. International Journal of Computer Applications, Volume 139 , No.11, 2016