Information Extraction from Text using Text Mining
Shabanaunnisa Begum
Shabanaunnisa Begum, Assistant Professor, Department of CSE, Malla Reddy Engineering College for Women, Maisammaguda, Dhulapally, Kompally, Medchal (M), Secunderabad (Telangana), India.
Manuscript received on 22 December 2020 | Revised Manuscript received on 12 January 2020 | Manuscript Published on 23 January 2020 | PP: 22-24 | Volume-9 Issue-2S5 December 2019 | Retrieval Number: B10061292S519/2019©BEIESP | DOI: 10.35940/ijitee.B1006.1292S519
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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: We’re living in an technology of extended strain and intellectual problems. The extended diploma of strain & force outcome in preference of to the form of people showing suicidal inclinations and therefore a larger shape of peoples are commit suicides pressure may be brought on able to family argument, task un satisfaction, healths troubles, and masses of others. inside the worldwide of modem computing, human beings feel loose to percentage their view and emotions over societal media for friends and family member via service together with text. to the kept nature and busy schedule of citizens it is pretty not easy to have interaction with pals and own relations people in individual, consequently community media structures are taken into consideration because of to the truth the maximum utilize platform for conversation. The purpose of this paper an estimate the suicidal incidents of a person thru using records mining technique to the textual content text somebody send to related humans. thru way of analyzing the additives of to the textual content messages we are capable of estimate the suicidal dispositions of a person without a doubt so important steps may be taken that allows you to keep the life of to the priority in this paper i referred to about text mining, tokenizations finding, emoji conversion, feeling assessment, estimation mining, KNNalgorithms.
Keywords: Textual Content Mining, Facts Discovery, Feeling Evaluation, Estimation Mining.
Scope of the Article: Text Mining