15. A system for automatically analyzing a conversation between a plurality ofusers, the system comprising: a data collection module on a computer comprising a processor and memory configured to receive signals corresponding to a training dataset including a plurality of data sequences related to the conversation; a feature extraction module on the computer configured to extract at least one feature from the received training dataset based on predefined feature categories; a learning module on the computer configured to: formulate a plurality of tasks for being learned from the training dataset based on the extracted at least one feature, wherein each of the plurality of tasks is related to at least one predefined label; provide a model for each of the plurality of formulated tasks, wherein the model includes one or more parameters having a set of parameters common to the plurality of formulated tasks, wherein the set of parameters includes at least one label dependency factor, that is an explicit parameter, which is explicitly shared with each of the plurality of formulated tasks; optimize values for the one or more parameters and the at least one explicit parameter to create an optimized model; and create a trained model for each of the plurality of formulated tasks using an optimized value of the at least one explicit parameter and corresponding values of the one or more parameters; and a classification module on the computer configured to assign the at least one predefined label for each of the plurality of formulated tasks on to a live dataset based on the created trained model, wherein the computer is configured to output signals corresponding to the live dataset assigned with the at least one predefined label for each of the plurality of formulated tasks.
15. A system for automatically analyzing a conversation between a plurality ofusers, the system comprising: a data collection module on a computer comprising a processor and memory configured to receive signals corresponding to a training dataset including a plurality of data sequences related to the conversation; a feature extraction module on the computer configured to extract at least one feature from the received training dataset based on predefined feature categories; a learning module on the computer configured to: formulate a plurality of tasks for being learned from the training dataset based on the extracted at least one feature, wherein each of the plurality of tasks is related to at least one predefined label; provide a model for each of the plurality of formulated tasks, wherein the model includes one or more parameters having a set of parameters common to the plurality of formulated tasks, wherein the set of parameters includes at least one label dependency factor, that is an explicit parameter, which is explicitly shared with each of the plurality of formulated tasks; optimize values for the one or more parameters and the at least one explicit parameter to create an optimized model; and create a trained model for each of the plurality of formulated tasks using an optimized value of the at least one explicit parameter and corresponding values of the one or more parameters; and a classification module on the computer configured to assign the at least one predefined label for each of the plurality of formulated tasks on to a live dataset based on the created trained model, wherein the computer is configured to output signals corresponding to the live dataset assigned with the at least one predefined label for each of the plurality of formulated tasks. 18. The system of claim 15 , wherein the predefined feature categories include at least one of a “Word 1-grams and 2-grams”, “Segment position in the conversation”, “Segment position in a conversation sequence”, “Sender”, “Contains email”, “#Upper case”, “# Punctuation”, “#Special punctuation”, “Positive Sentiment”, “Negative Sentiment”, “Category of previous sequence”, “Category of previous sequence of the same author”, and “Category of previous sequence of a different author”.