Abstract: Embodiments related to processing data sets in real-time by using a distributed network to generate and process partitioned streams. Messages are assigned to partition streams. Within each stream, each of a set of processors perform a designated task. Results from the task are transmitted (directly or indirectly) to another processor in the stream. The distributed and ordered processing can allow results to be transmitted while or before the results are stored.

Abstract: Embodiments related to processing data sets in real-time by using a distributed network to generate and process partitioned streams. Messages are assigned to partition streams. Within each stream, each of a set of processors perform a designated task. Results from the task are transmitted (directly or indirectly) to another processor in the stream. The distributed and ordered processing can allow results to be transmitted while or before the results are stored.

Abstract: Sentiment analyzer systems may include feedback analytics servers configured to receive and analyze feedback data from various client devices. Feedback data may be received and analyzed to determine feedback context and sentiment scores. In some embodiments, natural language processing neural networks may be used to determine sentiment scores for the feedback data. Feedback data also may be grouped into feedback aggregations based on context, and sentiment scores may be calculated for each feedback aggregation. Sentiment analyzer outputs and corresponding output devices may be determined based on the sentiment scores and feedback contexts.

Abstract: Embodiments related to processing data sets in real-time by using a distributed network to generate and process partitioned streams. Messages are assigned to partition streams. Within each stream, each of a set of processors perform a designated task. Results from the task are transmitted (directly or indirectly) to another processor in the stream. The distributed and ordered processing can allow results to be transmitted while or before the results are stored.

Abstract: Embodiments related to processing data sets in real-time by using a distributed network to generate and process partitioned streams. Messages are assigned to partition streams. Within each stream, each of a set of processors perform a designated task. Results from the task are transmitted (directly or indirectly) to another processor in the stream. The distributed and ordered processing can allow results to be transmitted while or before the results are stored.

Abstract: Embodiments related to processing data sets in real-time by using a distributed network to generate and process partitioned streams. Messages are assigned to partition streams. Within each stream, each of a set of processors perform a designated task. Results from the task are transmitted (directly or indirectly) to another processor in the stream. The distributed and ordered processing can allow results to be transmitted while or before the results are stored.

Abstract: Embodiments related to processing data sets in real-time by using a distributed network to generate and process partitioned streams. Messages are assigned to partition streams. Within each stream, each of a set of processors perform a designated task. Results from the task are transmitted (directly or indirectly) to another processor in the stream. The distributed and ordered processing can allow results to be transmitted while or before the results are stored.

Abstract: Sentiment analyzer systems may include feedback analytics servers configured to receive and analyze feedback data from various client devices. Feedback data may be received and analyzed to determine feedback context and sentiment scores. In some embodiments, natural language processing neural networks may be used to determine sentiment scores for the feedback data. Feedback data also may be grouped into feedback aggregations based on context, and sentiment scores may be calculated for each feedback aggregation. Sentiment analyzer outputs and corresponding output devices may be determined based on the sentiment scores and feedback contexts.

Abstract: Sentiment analyzer systems may include feedback analytics servers configured to receive and analyze feedback data from various client devices. Feedback data may be received and analyzed to determine feedback context and sentiment scores. In some embodiments, natural language processing neural networks may be used to determine sentiment scores for the feedback data. Feedback data also may be grouped into feedback aggregations based on context, and sentiment scores may be calculated for each feedback aggregation. Sentiment analyzer outputs and corresponding output devices may be determined based on the sentiment scores and feedback contexts.