Abstract: A computerized method is provided for automatically identifying a set of historical issues derived from historical customer interactions with an enterprise. The method includes collecting text-based data corresponding to the historical customer interactions, extracting customer queries from the text-based data, and normalizing and filtering the customer queries to generate the set of historical issues of the customer queries. The method also includes assigning the historical issues to one or more clusters that capture variances among the historical issues. The method further includes generating a legitimacy score for each historical issue and ranking the set of historical issues in accordance with their corresponding legitimacy scores. The method can further include identifying one or more issues in a transcript of unstructured text using the set of historical issues.

Abstract: A computerized method is provided for automatically identifying a set of historical issues derived from historical customer interactions with an enterprise. The method includes collecting text-based data corresponding to the historical customer interactions, extracting customer queries from the text-based data, and normalizing and filtering the customer queries to generate the set of historical issues of the customer queries. The method also includes assigning the historical issues to one or more clusters that capture variances among the historical issues. The method further includes generating a legitimacy score for each historical issue and ranking the set of historical issues in accordance with their corresponding legitimacy scores. The method can further include identifying one or more issues in a transcript of unstructured text using the set of historical issues.

Abstract: A self learning system and a method for analyzing the sentiments conveyed by an input text have been disclosed. The system includes a generator that generates an initial training set comprising a plurality of words linked to corresponding sentiments. The words and corresponding sentiments are stored in a repository. A rule based classifier segregates the input text into individual words, and compares the words with the entries in the repository, and subsequently determines a first score corresponding to the input text. The input text is also provided to a machine-learning based classifier that generates a plurality of features corresponding to the input text and subsequently generates a second score corresponding to the input text. The first score and the second score are further aggregated by an ensemble classifier which further generates a classification score indicative of the sentiment conveyed by the input text.