Table 7 Comparison of various approaches for mental health detection and sentiment analysis.

From: Multi task opinion enhanced hybrid BERT model for mental health analysis

Citation

Dataset

Problem statement

Proposed methodology

Accuracy

Kokane et al.42

Twitter dataset, Reddit dataset

Detecting mental illness using NLP Transformers on social media. Analyzing mental health status through text analysis on Twitter and Reddit.

DistilBERT

91% (Twitter), 84% (Reddit)

Chen et al.43

Hotel review datasets

Traditional CNN ignores contextual semantic information. Traditional RNN has information memory loss and vanishing gradient.

BERT + CNN + BiLSTM + Attention

92.35%

Selva Mary et al.44

User-generated content from Twitter, Facebook, and Instagram

Detecting depression signs in social media content. Enhancing early intervention and support for mental health challenges.

Bi-LSTM

98.5%

Sowbarnigaa et al.45

English language social media postings. Shared task introduced by ACL 2022.

Detecting signs of depression from social media postings. Utilizing sentiment analysis to categorize depression indicators.

CNN-LSTM

Precision: 93%

Atapattu et al.46

EmoMent corpus (2802 Facebook posts from Sri Lanka and India)

Detect mental health issues from text using NLP techniques. Develop emotion-annotated mental health corpus from South Asian countries.

RoBERTa

F1: 0.76, Macro F1: 0.77

Wu et al.47

NLPCC 2020 Shared Task 2 MAMS dataset

Re-formalize ABSA as a multi-aspect sentiment analysis task. Address the complexity of the MAMS dataset with Transformer-based Multi-aspect Modeling.

RoBERTa-TMM ensemble

F1: 85.24% (ATSA), F1: 79.41% (ACSA)

Our work

Mental health

The model aims to classify mental health-related text into status categories and sentiment labels using a multi-input neural network combining token embeddings, CNN, BiGRU, Transformer blocks, and attention mechanisms.

Opinion-BERT

Sentiment 96.25%, Status 93.74%