TY  - JOUR
PY  - 2019//
TI  - Tracking suicidal tendency using twitter data and machine learning algorithms
JO  - International journal of engineering and advanced technology
A1  - Daiphule, L.
A1  - Reddy, B.
A1  - Savith, A.
A1  - Apoorva, T.V.
SP  - 188
EP  - 191
VL  - 8
IS  - 5 SpecialIssue
N2  - Social media analytics has a major part in a person's life in this scenario. It is used to obtain the thoughts and opinion, sentiments of People. In this world people are comfortable sharing their thoughts and feelings effectively on social media rather than sharing their happiness or problems to their friends, parents or siblings'. Cerebral health indicators, with depression, Depression and nervousness leads to high risk of people obligating to suicide. Digital knowledge plays a major role to find suicidal tendency of people and to help them out. The study or research about finding the amount of people who have suicidal tendency or not was carried over by many universities where they collected the data from twitter or any health organizations. Twitter data is the most easily available data when compared to Facebook or any other social media site. These observations help us to determine the percentage of people having suicidal tendency or not by many processes which includes data preprocessing, data augmentation, testing and training, and final result representation. We use machine learning concepts. Sentiment Analysis or opinion mining is used. There are many reasons for suicides across the world, using this digital or social data and with the help of machine learning we could also differentiate between the group of people who actually are depressed or people tweeting jokes, songs etc. © BEIESP.<p /> <p>Language: en</p>
LA  - en
SN  - 2249-8958
UR  - http://dx.doi.org/
ID  - ref1
ER  -