Abstract: An analytics system receives, from a client computing device, a request to generate a presentation for an entity, the request indicating a presentation template to present one or more combinations of theme information, actionable insights information, and question information. The analytics system accesses feedback data associated with the entity. The feedback data comprises unstructured data available from multiple data stores. The analytics system determines analytical data that indicates themes, actionable insights, and questions generated from the feedback data. The analytics system generates a presentation file based on the analytical data. The presentation file includes one or more combinations of the themes, actionable insights, and questions according to the presentation template. The analytics system causes the presentation file to be transmitted to the client computing device.

Abstract: An analytics system receives, from a client computing device, a request to generate a presentation. The analytics system accesses one or more feedback datasets of feedback data. The feedback data comprises unstructured data available from multiple data stores. The analytics system generates, for each feedback dataset, a respective feedback text and a respective sentiment associated with the respective feedback text. The analytics system applies a model to a plurality of generated feedback texts to extract actionable insights. The actionable insights comprise portions of feedback texts that are (1) associated with items associated with an entity and (2) include a negative sentiment. The analytics system generates a presentation file that indicates the actionable insights.

Abstract: An analytics system receives, from a client computing device, a request to generate a presentation. The analytics system accesses one or more feedback datasets of feedback data. The feedback data comprises unstructured data available from multiple data stores. The analytics system generates, for each feedback dataset, a respective feedback text and a respective sentiment score indicating a degree of negativity associated with the respective feedback text. For a combination of a plurality of generated feedback texts, the analytics system selects a set of themes based at least on a plurality of generated sentiment scores. Each sentiment score of the plurality of generated sentiment scores is associated with one of the plurality of generated feedback texts. The analytics system generates a presentation file that indicates the set of themes. The analytics system causes the presentation file to be transmitted to the client computing device.

Abstract: A computerized method of representing customer interactions with an organization includes: receiving, by a computing device, customer web interaction data segments and customer conversation data segments; pre-processing the customer conversation data segments to remove specified types of information; scoring each of the pre-processed customer conversation data segments; pre-processing the customer web data interaction segments; extracting from the pre-processed customer web interaction data segments tokens; combining the pre-processed customer conversation data segments and the pre-processed customer web interaction data segments into a customer data set; parsing the customer data set into one or more windows; assigning, for each window, pre-trained weights to each of the tokens in each window; assigning a transaction theme to each window based on the tokens in each window; and generating, based on the transaction themes, a ranked list of topic keywords reflecting the customer web interaction data segments an

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 of populating one or more structured databases includes performing, by a computing device, the steps of: receiving customer message data from one or more data sources; extracting, from the customer message data, data sets representative of a set of customer questions; pre-processing, the data sets representative of the set of customer questions using one or more filters, thereby producing pre-processed data sets representative of customer questions; extracting, from the pre-processed data sets representative of customer questions, a set of customer expression data sets; grouping, the customer expression data sets into a set of clusters, each cluster representing one customer intent data set, each customer intent data set corresponding to one or more customer expression data sets; and storing, the customer intent data sets and the customer expression data sets in the structured database(s), the structured database(s) in electronic communication with the computing device.

Abstract: A computerized method of representing customer interactions with an organization includes: receiving, by a computing device, customer web interaction data segments and customer conversation data segments; pre-processing the customer conversation data segments to remove specified types of information; scoring each of the pre-processed customer conversation data segments; pre-processing the customer web data interaction segments; extracting from the pre-processed customer web interaction data segments tokens; combining the pre-processed customer conversation data segments and the pre-processed customer web interaction data segments into a customer data set; parsing the customer data set into one or more windows; assigning, for each window, pre-trained weights to each of the tokens in each window; assigning a transaction theme to each window based on the tokens in each window; and generating, based on the transaction themes, a ranked list of topic keywords reflecting the customer web interaction data segments an

Abstract: A computerized method of populating one or more structured databases includes performing, by a computing device, the steps of: receiving customer message data from one or more data sources; extracting, from the customer message data, data sets representative of a set of customer questions; pre-processing, the data sets representative of the set of customer questions using one or more filters, thereby producing pre-processed data sets representative of customer questions; extracting, from the pre-processed data sets representative of customer questions, a set of customer expression data sets; grouping, the customer expression data sets into a set of clusters, each cluster representing one customer intent data set, each customer intent data set corresponding to one or more customer expression data sets; and storing, the customer intent data sets and the customer expression data sets in the structured database(s), the structured database(s) in electronic communication with the computing device.

Abstract: Methods and apparatuses are described for analyzing unstructured computer text for identification and classification of complaint-specific interactions. A computer data stores unstructured text. A server computing device splits the unstructured text into phrases of words. The server generates a set of tokens from each phrase and removes tokens that are stopwords. The server generates a normalized sentiment score for each set of tokens. The server uses a rules-based classification engine to generate a rules-based complaint score for each set of tokens. The server uses an artificial intelligence machine learning model to generate a model-based complaint score for each set of tokens. The server determines determine whether each set of tokens corresponds to a complaint-specific interaction based upon the rules-based complaint score and the model-based complaint score.

Abstract: Methods and apparatuses are described for analyzing unstructured computer text for domain-specific stopword identification and removal. A computer data store stores unstructured text. A server computing device splits the unstructured text into phrases and generates tokens from the phrases. The server computing device generates a set of bootstrap keywords using the tokens. An artificial intelligence neural network executing on the server computing device generates a stopword training model. The server computing device generates a first set of candidate stopwords using the bootstrap keywords and the stopword training model. The server computing device generates regular expressions using the bootstrap keywords, and generates a second set of candidate stopwords using the regular expressions. The server computing device stores the candidate stopwords in the data store, and removes stopwords from the unstructured text using the data store.

Abstract: A computerized method of representing customer interactions with an organization includes: receiving, by a computing device, customer web interaction data segments and customer conversation data segments; pre-processing the customer conversation data segments to remove specified types of information; scoring each of the pre-processed customer conversation data segments; pre-processing the customer web data interaction segments; extracting from the pre-processed customer web interaction data segments tokens; combining the pre-processed customer conversation data segments and the pre-processed customer web interaction data segments into a customer data set; parsing the customer data set into one or more windows; assigning, for each window, pre-trained weights to each of the tokens in each window; assigning a transaction theme to each window based on the tokens in each window; and generating, based on the transaction themes, a ranked list of topic keywords reflecting the customer web interaction data segments an

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: Methods and apparatuses are described for analyzing unstructured computer text for theme generation to determine sentiment. A computer store stores unstructured text that is delimited, a searched phrases log, and a phrase click log. A computer server extracts phrases from the unstructured delimited text by splitting each line of the unstructured delimited text into one or more phrases. The computer server generates tokens from the unstructured delimited text, where the tokens comprise segments of the unstructured delimited text. The computer server determines one or more themes present in the unstructured delimited text.

Abstract: Methods and apparatuses are described for analyzing unstructured computer text for identification and classification of complaint-specific interactions. A computer data stores unstructured text. A server computing device splits the unstructured text into phrases of words. The server generates a set of tokens from each phrase and removes tokens that are stopwords. The server generates a normalized sentiment score for each set of tokens. The server uses a rules-based classification engine to generate a rules-based complaint score for each set of tokens. The server uses an artificial intelligence machine learning model to generate a model-based complaint score for each set of tokens. The server determines determine whether each set of tokens corresponds to a complaint-specific interaction based upon the rules-based complaint score and the model-based complaint score.

Abstract: Methods and apparatuses are described for analyzing unstructured computer text for domain-specific stopword identification and removal. A computer data store stores unstructured text. A server computing device splits the unstructured text into phrases and generates tokens from the phrases. The server computing device generates a set of bootstrap keywords using the tokens. An artificial intelligence neural network executing on the server computing device generates a stopword training model. The server computing device generates a first set of candidate stopwords using the bootstrap keywords and the stopword training model. The server computing device generates regular expressions using the bootstrap keywords, and generates a second set of candidate stopwords using the regular expressions. The server computing device stores the candidate stopwords in the data store, and removes stopwords from the unstructured text using the data store.

Abstract: Methods and apparatuses are described for analyzing unstructured computer text for theme generation to determine sentiment. A computer store stores unstructured text that is delimited, a searched phrases log, and a phrase click log. A computer server extracts phrases from the unstructured delimited text by splitting each line of the unstructured delimited text into one or more phrases. The computer server generates tokens from the unstructured delimited text, where the tokens comprise segments of the unstructured delimited text. The computer server determines one or more themes present in the unstructured delimited text.

Abstract: Methods and apparatus for analyzing, mapping and structuring healthcare data are disclosed. An example method includes parsing a clinical report to identify a parameter within the clinical report and associated with an image. The clinical report is associated with the image. The example method includes, when the parameter is identified in the parsing, analyzing the image to determine a value of the parameter, mapping the value, the image, and the clinical report to a model and generating an output to display one or more of the value, the image, or the clinical report.