Depression detection model based on social media post using sentiment analysis in Bengali language
Sadness is a basic human emotion. In our interview with four experts in psychology, they agreed that a person feeling sad for 14 days or longer can be said to be suffering from chronic sadness. Chronic sadness is a major symptom of depression. Numerous research studies have previously been conduc...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/113136/1/113136.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Sadness is a basic human emotion. In our interview with four experts in psychology,
they agreed that a person feeling sad for 14 days or longer can be said to be suffering
from chronic sadness. Chronic sadness is a major symptom of depression. Numerous
research studies have previously been conducted on the detection of depression. The
technique adopted for analysis in those studies is Lexicon-based (text) Analysis, which
is a popular type of Sentiment Analysis. Previous research on depression did not address
symptom identification. If we were to map text polarity in those studies with depressive
symptoms based on expert comments, then the symptoms identified in those research
studies would be mapped with sadness. However, there are several other potential
symptoms of depression that need to be considered, such as insomnia. To identify this
symptom, we propose the new DepSympdetect model. The technique used to develop
our model is Lexicon-based Analysis with a linguistic dictionary. Our model is focused
on the Bengali language. The main reason for choosing Bengali is that it is one of the
most widely spoken languages in the world. Another reason is that no such study has
ever been conducted in Bengali to identify depressive symptoms using a linguistic
dictionary approach. We enhanced the existing linguistic dictionary that was compiled
through an opinion review from a past research study in the Bengali language, resulting
in the addition of more than 300 new negative sentiment words and 45-degree modifiers.
This expanded linguistic dictionary will enable us to identify more negative words for
depression analysis. For data collection, we first obtained the consent of ten participants.
Then, we manually crawled their Facebook profiles and groups for posts. In our
improved DepSympdetect model, we used a number of new features such as text
polarity, statement polarity and time of post. With the help of these features, we
successfully identified two depressive symptoms, which are chronic sadness and
insomnia. We also sought experts to verify the model and the dataset. The experts
provided help by validating the newly added negative sentiment words and the degree
modifiers. We measured the accuracy of DepSymdetect model that was 93.33%. |
|---|