Abstract: Methods, systems, and computer program products for applying multi-level clustering at scale to unlabeled data for anomaly detection and security are disclosed. A computer-implemented method may include receiving transactional data associated with a plurality of user accounts, analyzing the transactional data of the accounts in view of a clustering model, associating each of the accounts with one of multiple peer groups from the clustering model, detecting anomalous account behavior in a peer group in view of a scatteredness score computed for each account in the peer group where each scatteredness score is computed based on a neighborhood of accounts in the peer group determined for each respective account of the peer group, and creating a report comprising account and scatteredness score information for one or more of the accounts in the peer group associated with detected anomalous account behavior.

Abstract: Reconciling detailed transaction feedback by detecting a rating of a transaction, where the rating indicates a negative experience, mining the sentiment of words in feedback text that is included with or as part of the rating to detect whether the words indicate positive sentiment or negative sentiment, responsive to determining that the words in the feedback text indicate that the feedback text connotes a positive sentiment, adjusting the rating of the transaction. The mining may include testing words in the feedback text to detect whether the words indicate positive sentiment or negative sentiment by calculating a sentiment score.

Abstract: Methods, systems, and computer program products for applying multi-level clustering at scale to unlabeled data for anomaly detection and security are disclosed. A computer-implemented method may include receiving transactional data associated with a plurality of user accounts, analyzing the transactional data of the accounts in view of a clustering model, associating each of the accounts with one of multiple peer groups from the clustering model, detecting anomalous account behavior in a peer group in view of a scatteredness score computed for each account in the peer group where each scatteredness score is computed based on a neighborhood of accounts in the peer group determined for each respective account of the peer group, and creating a report comprising account and scatteredness score information for one or more of the accounts in the peer group associated with detected anomalous account behavior.

Abstract: Reconciling detailed transaction feedback by detecting a rating of a transaction, where the rating indicates a negative experience, mining the sentiment of words in feedback text that is included with or as part of the rating to detect whether the words indicate positive sentiment or negative sentiment, responsive to determining that the words in the feedback text indicate that the feedback text connotes a positive sentiment, adjusting the rating of the transaction. The mining may include testing words in the feedback text to detect whether the words indicate positive sentiment or negative sentiment by calculating a sentiment score.

Abstract: Reconciling detailed transaction feedback by detecting a rating of a transaction, where the rating indicates a negative experience, mining the sentiment of words in feedback text that is included with or as part of the rating to detect whether the words indicate positive sentiment or negative sentiment, responsive to determining that the words in the feedback text indicate that the feedback text connotes a positive sentiment, adjusting the rating of the transaction. The mining may include testing words in the feedback text to detect whether the words indicate positive sentiment or negative sentiment by calculating a sentiment score.

Abstract: Reconciling detailed transaction feedback by detecting a rating of a transaction, where the rating indicates a negative experience, mining the sentiment of words in feedback text that is included with or as part of the rating to detect whether the words indicate positive sentiment or negative sentiment, responsive to determining that the words in the feedback text indicate that the feedback text connotes a positive sentiment, adjusting the rating of the transaction. The mining may include testing words in the feedback text to detect whether the words indicate positive sentiment or negative sentiment by calculating a sentiment score.

Abstract: Reconciling detailed transaction feedback by detecting a rating of a transaction, where the rating indicates a negative experience, mining the sentiment of words in feedback text that is included with or as part of the rating to detect whether the words indicate positive sentiment or negative sentiment, responsive to determining that the words in the feedback text indicate that the feedback text connotes a positive sentiment, adjusting the rating of the transaction. The mining may include testing words in the feedback text to detect whether the words indicate positive sentiment or negative sentiment by calculating a sentiment score.

Abstract: Reconciling detailed transaction feedback by detecting a rating of a transaction, where the rating indicates a negative experience, mining the sentiment of words in feedback text that is included with or as part of the rating to detect whether the words indicate positive sentiment or negative sentiment, responsive to determining that the words in the feedback text indicate that the feedback text connotes a positive sentiment, adjusting the rating of the transaction. The mining may include testing words in the feedback text to detect whether the words indicate positive sentiment or negative sentiment by calculating a sentiment score.