aims to ascertain the signs of depression of a person from their messages and posts on social media wherein people share their feelings and emotions. Given social media postings in English, the system should classify the signs of depression into three labels namely "not depressed", "moderately depressed", and "severely depressed". To achieve this objective, we have adopted a fine-tuned BERT model. This solution from team SSN_MLRG1 achieves 58.5%

Task 10 in SemEval 2022 is a composite task which entails analysis of opinion tuples, and recognition and demarcation of their nature. In this paper, we will elaborate on how such a methodology is implemented, how it is undertaken for a Structured Sentiment Analysis, and the results obtained thereof. To achieve this objective, we have adopted a bi-layered BiLSTM approach. In our research, a variation on the norm has been effected towards enhancement of accuracy, by basing the categorization meted out to an individual member as a by-product of its adjacent members, using specialized algorithms to ensure the veracity of the output, which has been modelled to be the holistically most accurate label for the entire sequence.Such a strategy is superior in terms of its parsing accuracy and requires less time. This manner of action has yielded an SF1 of 0.33 in the highest-performing configuration.