메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술대회자료
저자정보
Sergazy Narynov (Alem Research) Daniyar Mukhtarkhanuly (Alem Research) Batyrkhan Omarov (International Information Technology University) Kanat Kozhakhmet (Open University of Kazakhstan) Bauyrzhan Omarov (Al-Farabi Kazakh National University)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2020
발행연도
2020.10
수록면
6 - 11 (6page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
According to the latest who data published in 2017, the number of suicides in Kazakhstan was 4855, or 3.55% of the total number of deaths. The age-adjusted death rate is 27.74 per 100,000 population. Kazakhstan is ranked 4th in the world by this indicator. This article compares machine learning algorithms with and without a teacher to identify depressive content in social media posts, with a focus on hopelessness and psychological pain for semantic analysis as key causes of suicide. Suicide is not spontaneous, and preparation for suicide can last about a year, during which time a person will show signs of their condition in our case by posting depressive content on their social network profile. This algorithm helps in detecting depressive content that can cause suicide to help people find confident help from psychologists at the national center for suicide prevention in Kazakhstan. Having obtained the highest score for 95% of the f1 score for a random forest (training with a teacher) with the tf-idf vectorization model, we can conclude by saying that the K-means algorithm(training without a teacher) using tf-idf shows impressive results that are only 4% lower in f1 and accuracy.

목차

Abstract
1. INTRODUCTION
2. LITERATURE REVIEW
3. MATERIALS AND METHODS
4. RESULTS
CONCLUSION
REFERENCES

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2020-003-001571127