23. A method for automated analysis of one or more conversations exchanged between a customer care agent and multiple customers of a social media platform, the method comprising: collecting, by one or more processors, a first training dataset having a natural language text sequence of a conversation exchanged between the customer care agent and at least one of the multiple customers of the social media platform; extracting, by the one or more processors, one or more pre-defined features stored at a database from the first text sequence; defining, by the one or more processors, at least one equation that includes a common parameter for each of two or more tasks to be resolved by the customer care agent; based on the extracted features, calculating, by the one or more processors, a first parameter value for a first task of the two or more tasks and a second parameter value for a second task of the two or more tasks using the equations defined for the two or more tasks; assigning, by the one or more processors, one or more labels to the first text sequence based on the calculated first parameter value and the calculated second parameter value, a first label of the one or more labels being an issue status label and a second label of the one or more labels being a nature of a conversation status label; sending, by the one or more processors, a status update to the customer care agent indicating a resolved status based on the one or more labels assigned to the first text sequence and the extracted features.
23. A method for automated analysis of one or more conversations exchanged between a customer care agent and multiple customers of a social media platform, the method comprising: collecting, by one or more processors, a first training dataset having a natural language text sequence of a conversation exchanged between the customer care agent and at least one of the multiple customers of the social media platform; extracting, by the one or more processors, one or more pre-defined features stored at a database from the first text sequence; defining, by the one or more processors, at least one equation that includes a common parameter for each of two or more tasks to be resolved by the customer care agent; based on the extracted features, calculating, by the one or more processors, a first parameter value for a first task of the two or more tasks and a second parameter value for a second task of the two or more tasks using the equations defined for the two or more tasks; assigning, by the one or more processors, one or more labels to the first text sequence based on the calculated first parameter value and the calculated second parameter value, a first label of the one or more labels being an issue status label and a second label of the one or more labels being a nature of a conversation status label; sending, by the one or more processors, a status update to the customer care agent indicating a resolved status based on the one or more labels assigned to the first text sequence and the extracted features. 24. The method of claim 23 , wherein the first text sequence of the conversation includes one or more sentences that form a training dataset, wherein the training dataset is received by the one or more processors in real time.