Aspect-Based Sentiment Classification for Detecting the Cognitive Triad Mechanism of Depression

The cognitive triad mechanism of Beck’s theory is critical for early diagnosis and prognosis depression. According to the mechanism, negative views about self, future, world appear be routine in depressed people, occurring spontaneously. challenge clinical interview that individuals may have trouble explaining their past current symptoms a variety factors, such as existence concurrent mental health or medical problems memory difficulties. aspect-based sentiment classification technique can help psychologists overcome this difficulty by identifying pairs {(self, negative), (future, (world, negative)} from individual's chatbot social media messages. proposed multilayer RNN-capsule architecture on Cognitive Triad Dataset (CTD) consists two layers, i.e., recognition aspect identification layers. Through experiments CTD, model outperforms single-layer classification. accuracy F1 score are 0.857 0.858, respectively. capsule 0.89 0.892, while detection 0.957 0.956. Also, most its counterparts like GNN, LSTM, BiLSTM. More importantly, capable producing words containing inclinations reflect attributes capsules, respectively, without use any linguistic knowledge.