Earticle

초록

영어

Mental health problems leading to depression have become a critical concern due to the growing engagement of people on social media platforms. Several past approaches have been implemented by analyzing the pattern and behaviour of the posts by users on social networking sites. This research study proposed a system for predicting users who may be depressed, based on the characteristics of users who is already affected. A combination of both the tweet-level and the user-level architecture was used to generate a more robust and reliable system where semantic embeddings trained from advanced neural networks were adopted under the tweet-level. SVM with Word2Vec and TF-IDF has been used and yielded an accuracy of 98.14% and recall of 95.63%.

목차

ABSTRACT
1. Overview of Mental Health Emotions
2. Related Research
3. Methodology and Experiemental Setup
3.1 Loading Libraries
3.2 Dataset
3.3 Data splitting
3.4 Data Preprocessing
3.5 Feature Extractions
3.6 Classification Techniques
4. Results
5. Conclusion
Acknowledgments
References

키워드

저자

• Shreya Reddy [ Graduate Student, Computer ScienceDepartment, Lakehead University, Canada. ] Corresponding Author
• Sabah Mohammed [ Professor, Computer ScienceDepartment, Lakehead University, Canada. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  컴퓨터게임및콘텐츠논문지(구 한국컴퓨터게임학회논문지) [Journal of Computer Games and Contents]

• 간기

  월간

• pISSN

  3091-7409

• eISSN

  3092-3638

• 수록기간

  2002~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 691 DDC 793