Natural language processing using joint topic-sentiment detection

There is a need for solutions for more effective and efficient natural language processing systems. This need can be addressed, for example, by a system configured to obtain a term correlation data object for a plurality of digital documents; determine, based at least in part on the term correlation data object, a term-topic correlation data object for the plurality of digital documents; determine, based at least in part on the term-topic correlation data object, a document-topic correlation data object for the plurality of digital documents; determine, based at least in part on the term-topic correlation data object, a document-sentiment correlation data object for the plurality of digital documents; generate the topic detection based at least in part on the document-topic correlation object; and generate the sentiment detection based at least in part on the document-sentiment correlation object.

BACKGROUND

Many existing natural language processing (NLP) systems face technical challenges in accurately and efficiently detecting reliable properties for relatively short NLP input data. One reason behind the noted technical challenges is that, because of their limited size, relatively short NLP data produce limited valuable input NLP feature data. Through ingenuity and innovation, various embodiments of the present invention make substantial improvements to efficiency and reliability of NLP systems, including by improving capabilities of NLP systems to address technical challenges in accurately and efficiently detecting reliable properties for relatively short NLP input data.

BRIEF SUMMARY

In general, embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for NLP using sentiment detection. Certain embodiments utilize systems, methods, and computer program products that enable NLP by detecting joint sentiment-topic models for NLP input data.

In accordance with one aspect, a method is provided. In one embodiment, the method comprises obtaining, by one or more processors, a term correlation data object for a plurality of digital documents, wherein: (1) the term correlation data object comprises a plurality of term correlation indicators for a plurality of terms, and (2) the plurality of term correlation indicators describe co-occurrences of the plurality of terms in the plurality of digital documents; determining, by the one or more processors and based at least in part on the term correlation data object, a term-topic correlation data object for the plurality of digital documents, wherein: (1) the term-topic correlation data object comprises a plurality of term-topic correlation indicators, (2) the plurality of term-topic correlation indicators describe relationships between the plurality of terms and a plurality of topics, (3) the term-topic correlation data object is determined by a term-topic factorization of the term correlation data object into a plurality of inferred term-topic data objects, and (4) the plurality of inferred term-topic data objects comprises the term-topic correlation data object; determining, by the one or more processors and based at least in part on the term-topic correlation data object, a document-topic correlation data object for the plurality of digital documents, wherein: (1) the document-topic correlation data object comprises a plurality of document-topic correlation indicators, (2) the plurality of digital documents-topic correlation indicators describe relationships between the plurality of digital documents and a plurality of topics, (3) the document-topic correlation data object is determined by a document-topic factorization of a source term-document data object into a plurality of inferred document-topic data objects, and (4) the plurality of inferred document-topic data objects comprises the document-topic correlation data object; determining, by the one or more processors and based at least in part on the term-topic correlation data object, a document-sentiment correlation data object for the plurality of digital documents, wherein: (1) the document-sentiment correlation data object comprises a plurality of document-sentiment correlation indicators, (2) the plurality of digital documents-sentiment correlation indicators describe relationships between the plurality of digital documents and a plurality of sentiments, (3) the document-topic correlation data object is determined by a document-sentiment factorization of a source document-sentiment data object into a plurality of inferred document-sentiment data objects, and (4) the plurality of inferred document-topic data objects comprises the document-sentiment correlation data object; generating, by the one or more processors, the topic detection based at least in part on the document-topic correlation object; and generating, by the one or more processors, the sentiment detection based at least in part on the document-sentiment correlation object.

In accordance with another aspect, a computer program product is provided. The computer program product may comprise at least one computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising executable portions configured to obtain, by one or more processors, a term correlation data object for a plurality of digital documents, wherein: (1) the term correlation data object comprises a plurality of term correlation indicators for a plurality of terms, and (2) the plurality of term correlation indicators describe co-occurrences of the plurality of terms in the plurality of digital documents; determine, by the one or more processors and based at least in part on the term correlation data object, a term-topic correlation data object for the plurality of digital documents, wherein: (1) the term-topic correlation data object comprises a plurality of term-topic correlation indicators, (2) the plurality of term-topic correlation indicators describe relationships between the plurality of terms and a plurality of topics, (3) the term-topic correlation data object is determined by a term-topic factorization of the term correlation data object into a plurality of inferred term-topic data objects, and (4) the plurality of inferred term-topic data objects comprises the term-topic correlation data object; determine, by the one or more processors and based at least in part on the term-topic correlation data object, a document-topic correlation data object for the plurality of digital documents, wherein: (1) the document-topic correlation data object comprises a plurality of document-topic correlation indicators, (2) the plurality of digital documents-topic correlation indicators describe relationships between the plurality of digital documents and a plurality of topics, (3) the document-topic correlation data object is determined by a document-topic factorization of a source term-document data object into a plurality of inferred document-topic data objects, and (4) the plurality of inferred document-topic data objects comprises the document-topic correlation data object; determine, by the one or more processors and based at least in part on the term-topic correlation data object, a document-sentiment correlation data object for the plurality of digital documents, wherein: (1) the document-sentiment correlation data object comprises a plurality of document-sentiment correlation indicators, (2) the plurality of digital documents-sentiment correlation indicators describe relationships between the plurality of digital documents and a plurality of sentiments, (3) the document-topic correlation data object is determined by a document-sentiment factorization of a source document-sentiment data object into a plurality of inferred document-sentiment data objects, and (4) the plurality of inferred document-topic data objects comprises the document-sentiment correlation data object; generate, by the one or more processors, the topic detection based at least in part on the document-topic correlation object; and generate, by the one or more processors, the sentiment detection based at least in part on the document-sentiment correlation object.

In accordance with yet another aspect, an apparatus comprising at least one processor and at least one memory including computer program code is provided. In one embodiment, the at least one memory and the computer program code may be configured to, with the processor, cause the apparatus to obtain, by one or more processors, a term correlation data object for a plurality of digital documents, wherein: (1) the term correlation data object comprises a plurality of term correlation indicators for a plurality of terms, and (2) the plurality of term correlation indicators describe co-occurrences of the plurality of terms in the plurality of digital documents; determine, by the one or more processors and based at least in part on the term correlation data object, a term-topic correlation data object for the plurality of digital documents, wherein: (1) the term-topic correlation data object comprises a plurality of term-topic correlation indicators, (2) the plurality of term-topic correlation indicators describe relationships between the plurality of terms and a plurality of topics, (3) the term-topic correlation data object is determined by a term-topic factorization of the term correlation data object into a plurality of inferred term-topic data objects, and (4) the plurality of inferred term-topic data objects comprises the term-topic correlation data object; determine, by the one or more processors and based at least in part on the term-topic correlation data object, a document-topic correlation data object for the plurality of digital documents, wherein: (1) the document-topic correlation data object comprises a plurality of document-topic correlation indicators, (2) the plurality of digital documents-topic correlation indicators describe relationships between the plurality of digital documents and a plurality of topics, (3) the document-topic correlation data object is determined by a document-topic factorization of a source term-document data object into a plurality of inferred document-topic data objects, and (4) the plurality of inferred document-topic data objects comprises the document-topic correlation data object; determine, by the one or more processors and based at least in part on the term-topic correlation data object, a document-sentiment correlation data object for the plurality of digital documents, wherein: (1) the document-sentiment correlation data object comprises a plurality of document-sentiment correlation indicators, (2) the plurality of digital documents-sentiment correlation indicators describe relationships between the plurality of digital documents and a plurality of sentiments, (3) the document-topic correlation data object is determined by a document-sentiment factorization of a source document-sentiment data object into a plurality of inferred document-sentiment data objects, and (4) the plurality of inferred document-topic data objects comprises the document-sentiment correlation data object; generate, by the one or more processors, the topic detection based at least in part on the document-topic correlation object; and generate, by the one or more processors, the sentiment detection based at least in part on the document-sentiment correlation object.

DETAILED DESCRIPTION

II. EXEMPLARY SYSTEM ARCHITECTURE

FIG. 1provides an exemplary overview of an architecture100that can be used to practice embodiments of the present invention. The architecture100may include an NLP system101and one or more external computing entities102, where the one or more external computing entities102provide NLP input data (e.g., labeled NLP input data and/or unlabeled NLP input data) to the NLP system101, and further where the NLP system101generates joint topic-sentiment detections for the NLP input data and provides the joint topic-sentiment detections to the one or more external entities. In some embodiments, the NLP system101interacts with the one or more external computing entities102over a communication network (not shown). The communication network may include any wired or wireless communication network including, for example, a wired or wireless local area network (LAN), personal area network (PAN), metropolitan area network (MAN), wide area network (WAN), or the like, as well as any hardware, software and/or firmware required to implement it (such as, e.g., network routers, and/or the like).

The NLP system101may include an NLP computing entity106and a storage subsystem108. The NLP computing entity106is configured to generate joint sentiment-detections for the NLP input data stored in the storage subsystem108. The storage subsystem108may include a labeled data storage unit121and an unlabeled data storage unit122. The NLP computing entity106may include a term correlation modeling engine111, a term-topic modeling engine112, a topic detection engine113, and a sentiment detection engine114.

The labeled data storage unit121is configured to store labeled NLP input data items, where labeled each NLP input data item may include an NLP input data item (e.g., a digital document with NLP data) and an associated prior sentiment label for the NLP input data item. The prior sentiment label for an NLP input data item may be an initial sentiment score for the NLP input data item determined using one or more shallow sentiment labeling techniques. The unlabeled data storage unit122is configured to store unlabeled NLP input data items, where each unlabeled NLP input data item may include an NLP data item that is not associated with a prior sentiment label for the NLP input data item. Each of the labeled data storage unit121and the unlabeled data storage unit122may include one or more non-volatile storage or memory media including but not limited to hard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipede memory, racetrack memory, and/or the like.

The term correlation modeling engine111is configured to process the NLP input data stored in the storage subsystem (e.g., including the labeled NLP input data items stored in labeled data storage unit121and/or the unlabeled NLP input data items stored in unlabeled data storage unit122) to generate term correlation data for the NLP input data, as the term is further described below. The term-topic modeling engine112is configured to process the term correlation data generated by the term correlation modeling engine111to generate term-topic correlation data for the NLP input data, as the term is further described below. The topic detection engine113is configured to process the term-topic correlation data generated by the term-topic modeling engine112to generate document-topic correlation data for the NLP input data, as the term is further described below. The sentiment detection engine114is configured to process the term-topic correlation data generated by the term-topic modeling engine112to generate document-sentiment correlation data for the NLP input data, as the term is further described below. The NLP computing entity106may further may include engines (not depicted) configured to generate topic detections for the NLP input data based on the document-topic correlation data generated by the topic detection engine113and/or to generate sentiment detections for the NLP input data based on the document-sentiment correlation data generated by the sentiment detection engine.

In some embodiments, the NLP input data stored in the storage subsystem108may include feedback data, such as provider feedback data associated with a healthcare with a healthcare provider institution and/or a health insurance provider system. Moreover, in some embodiments, the NLP computing entity106is configured to process the feedback data to generate joint topic-sentiment detections that identify subject matters related to critical feedback and/or subject matters related to positive feedback.

A. Exemplary NLP Computing Entity

As indicated, in one embodiment, the NLP computing entity106may also may include one or more communications interfaces220for communicating with various computing entities, such as by communicating data, content, information, and/or similar terms used herein interchangeably that can be transmitted, received, operated on, processed, displayed, stored, and/or the like.

In one embodiment, the NLP computing entity106may further may include or be in communication with non-volatile media (also referred to as non-volatile storage, memory, memory storage, memory circuitry and/or similar terms used herein interchangeably). In one embodiment, the non-volatile storage or memory may include one or more non-volatile storage or memory media210, including but not limited to hard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipede memory, racetrack memory, and/or the like. As will be recognized, the non-volatile storage or memory media may store databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like. The term database, database instance, database management system, and/or similar terms used herein interchangeably may refer to a collection of records or data that is stored in a computer-readable storage medium using one or more database models, such as a hierarchical database model, network model, relational model, entity-relationship model, object model, document model, semantic model, graph model, and/or the like.

B. Exemplary External Computing Entity

FIG. 3provides an illustrative schematic representative of an external computing entity102that can be used in conjunction with embodiments of the present invention. In general, the terms device, system, computing entity, entity, and/or similar words used herein interchangeably may refer to, for example, one or more computers, computing entities, desktops, mobile phones, tablets, phablets, notebooks, laptops, distributed systems, kiosks, input terminals, servers or server networks, blades, gateways, switches, processing devices, processing entities, set-top boxes, relays, routers, network access points, base stations, the like, and/or any combination of devices or entities adapted to perform the functions, operations, and/or processes described herein. External computing entities110can be operated by various parties. As shown inFIG. 3, the external computing entity102can may include an antenna312, a transmitter304(e.g., radio), a receiver306(e.g., radio), and a processing element308(e.g., CPLDs, microprocessors, multi-core processors, coprocessing entities, ASIPs, microcontrollers, and/or controllers) that provides signals to and receives signals from the transmitter304and receiver306, respectively.

The signals provided to and received from the transmitter304and the receiver306, respectively, may include signaling information/data in accordance with air interface standards of applicable wireless systems. In this regard, the external computing entity102may be capable of operating with one or more air interface standards, communication protocols, modulation types, and access types. More particularly, the external computing entity102may operate in accordance with any of a number of wireless communication standards and protocols, such as those described above with regard to the NLP computing entity106. In a particular embodiment, the external computing entity102may operate in accordance with multiple wireless communication standards and protocols, such as UMTS, CDMA2000, 1×RTT, WCDMA, GSM, EDGE, TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi, Wi-Fi Direct, WiMAX, UWB, IR, NFC, Bluetooth, USB, and/or the like. Similarly, the external computing entity102may operate in accordance with multiple wired communication standards and protocols, such as those described above with regard to the NLP computing entity106via a network interface320.

Via these communication standards and protocols, the external computing entity102can communicate with various other entities using concepts such as Unstructured Supplementary Service Data (USSD), Short Message Service (SMS), Multimedia Messaging Service (MIMS), Dual-Tone Multi-Frequency Signaling (DTMF), and/or Subscriber Identity Module Dialer (SIM dialer). The external computing entity102can also download changes, add-ons, and updates, for instance, to its firmware, software (e.g., including executable instructions, applications, program modules), and operating system.

The external computing entity102may also comprise a user interface (that can may include a display316coupled to a processing element308) and/or a user input interface (coupled to a processing element308). For example, the user interface may be a user application, browser, user interface, and/or similar words used herein interchangeably executing on and/or accessible via the external computing entity102to interact with and/or cause display of information/data from the NLP computing entity106, as described herein. The user input interface may comprise any of a number of devices or interfaces allowing the external computing entity102to receive data, such as a keypad318(hard or soft), a touch display, voice/speech or motion interfaces, or other input device. In embodiments including a keypad318, the keypad318can may include (or cause display of) the conventional numeric (0-9) and related keys (#, *), and other keys used for operating the external computing entity102and may include a full set of alphabetic keys or set of keys that may be activated to provide a full set of alphanumeric keys. In addition to providing input, the user input interface can be used, for example, to activate or deactivate certain functions, such as screen savers and/or sleep modes.

In another embodiment, the external computing entity102may include one or more components or functionality that are the same or similar to those of the NLP computing entity106, as described in greater detail above. As will be recognized, these architectures and descriptions are provided for exemplary purposes only and are not limiting to the various embodiments.

Discussed herein methods, apparatus, systems, computing devices, computing entities, and/or the like for NLP analysis using sentiment detection configured to generate joint sentiment-topic detections. As will be recognized, however, the disclosed concepts can be used to perform other types of NLP analysis, including NLP analyses configured to generate non-joint NLP property predictions as well as NLP analyses configured to generate joint NLP property predictions other than joint sentiment-topic detections.

A. Technical Problems

Many existing NLP systems face substantial technical challenges in accurately and efficiently detecting reliable properties for relatively short NLP input data, such as detecting topics for with relatively short NLP input data and/or detecting sentiments for relatively short NLP input data. One reason behind the noted technical challenges is that, because of their limited size, relatively short NLP data produce limited valuable input feature data that can be used to accurately and efficiently detect reliable properties for relatively short NLP input data. As a result, existing NLP systems face substantial challenges with effective and efficient feature extraction from relatively short NLP input data. To combat challenges associated with limited feature extraction potentials of relatively short NLP input data, some existing NLP systems rely on computationally inefficient calculations that often require substantial storage bandwidth. Moreover, some existing NLP systems extract NLP features from relatively short NLP input data that are not sufficiently indicative of reliable properties of such data, and thus fail to provide reliable solutions for effectively and accurately reliable properties for relatively short NLP input data.

An example of relatively short NLP input data is feedback data associated with many healthcare systems. Many cooperative healthcare systems aim to transform health care delivery into a mission-driven, patient-centered, value-enhancing system of care. Feedback data, such as patient feedback data and/or provider feedback data could serve as a valuable yardstick for this transformation process. By mining such feedback data, an NLP system can extract customers' sentiment and opinion towards the quality of service they have received from the healthcare providers. However, because of the limitations of many existing NLP systems in accurately and efficiently detecting reliable properties for relatively short NLP input data, they are ill-suited for feedback processing in healthcare systems. As the discussed examples demonstrate, many existing NLP systems face technical challenges in accurately and efficiently detecting reliable properties for relatively short feedback data associated with healthcare systems.

Moreover, many existing NLP systems face significant technical drawbacks in accurately analyzing sentiment information about feedback data to detect relevant semantic properties for such feedback data. For example, many existing NLP systems use supervised learning based on preexisting sentiment-labeled NLP documents to generate sentiment labels for new NLP documents. However, the generated sentiment labels are often poor descriptors of overall semantic structure of the NLP documents. In some cases, while existing NLP systems can provide some useful sentiment information about feedback data, they fail to properly categorize such sentiments in the context of subject-matter-specific features of such feedback data. Thus, many existing NLP systems face technical challenges related to accurately utilizing sentiment information about feedback data in order to detect relevant semantic properties for such feedback data.

For example, a naïve sentiment label for customer feedback saying “coordinate the request with the doctor's office directly instead of me being the middle man” that fails to detect subject matter of the customer feedback (e.g., related to a doctor's visit) will likely fail to properly detect relevant sentiments of the customer feedback. As another example, a sentiment detection for the feedback “the report is with the post office and mail man handled it beautifully” that fails to detect subject matter of the feedback (e.g., related to report mailing) will similarly fail to properly detect reliable properties for the feedback based on the sentiment analysis of the feedback. As the discussed examples demonstrate, many existing NLP systems face technical drawbacks in accurately analyzing feedback data to detect relevant semantic properties.

B. Technical Solutions

Various embodiments of the present invention address technical challenges related to accurately and efficiently detecting reliable properties for relatively short NLP input data, including feedback data associated many healthcare systems, by utilizing NLP models that detect important features from NLP data using groups of interconnected factorization-based optimizations. For example, various embodiments of the present invention use optimization of non-negative matrix factorization (NMF) models and/or non-negative matrix tri-factorization (3-factor NMF) models. By utilizing NMF models and/or 3-factor NMF models, various embodiments of the present invention effectively and efficiently relate raw feature data associated with NLP data to more sophisticated correlation models that describe semantically deeper inferences about the NLP data. In doing so, various embodiments of the present invention address technical drawbacks of many existing NLP systems in accurately and efficiently detecting reliable properties for relatively short NLP input data, including feedback data associated many healthcare systems.

For example, in some embodiments, a disclosed NLP system performs a constrained 3-factor NMF of a term-document correlation matrix which can be implemented using simple update rules, where the constraints in the mentioned optimization problem may include inequality constraints on sentiment values of feedback data and a subset of pre-existing labeled feedback data (e.g., with real-valued sentiment scores). In doing so, the noted embodiments enable integrating domain-specific data (e.g., the subset of pre-existing labeled feedback data) in addition to input feature data to generate feature values for feedback documents. As a result, the disclosed NLP systems utilize minimal feature data to infer substantial and important conclusions about semantic (e.g., sentimental and/or topical) structure of feedback data. This in turn provides solutions for efficient and effective detection of reliable properties for relatively short NLP input data, including feedback data associated many healthcare systems.

In some embodiments, disclosed NLP systems address relative shortness of NLP input data by factoring feature data (e.g., term correlation data for the feedback data) for the NLP input data using NMF-based optimization to generate term-topic correlation data, rather by utilizing high-dimensional and sparse term occurrence information associated with the NLP input data to generate term-topic correlation data. This alone provides substantial efficiency advantages (e.g., by reducing the need for utilizing storage-intensive and computationally-complex data such as the high-dimensional and sparse term occurrence information associated with NLP input data) as well as substantial accuracy advantages (e.g., by providing extraction of more reliable and more semantically representative features associated with NLP input data) compared to many existing NLP systems. Moreover, in some embodiments, subsequent to the above-noted NMF-based optimization to generate term-topic correlation data, the disclosed NLP systems detect document-topic correlation data from the term-document correlation data using another NMF-based optimization, thus further expanding the efficiency and accuracy advantages offered by various embodiments of the present invention in accurately and efficiently detecting reliable properties for relatively short NLP input data, including feedback data associated many healthcare systems.

In some embodiments, disclosed NLP systems use an optimization based on a 3-factor NMF model to learn sentiment values for unlabeled texts in a manner that allows for integrating domain-specific data (e.g., prior sentiment labels, such as initial sentiment labels by a Clarabridge kind of sentiment analysis engine) in calculating document-sentiment correlation data. This in turn creates an efficient and effective sentiment classification system for relatively short NLP input data, including feedback data associated many healthcare systems. Indeed, experimental results show that some aspects of the disclosed techniques achieve around 80% accuracy in both topic detection and sentiment detection, thus outperforming many existing NLP systems.

Moreover, various embodiments of the present invention address technical challenges related to accurately analyzing sentiment information about feedback data to detect relevant semantic properties for such feedback data by jointly detecting topic and sentiment designations for such feedback data. In some embodiments, disclosed NLP systems perform topic detection using constrained-NMF-based models and perform sentiment detection using constrained-3-factor-NMF-based models. In doing so, the disclosed NLP systems provide solutions for accurately detecting relevant semantic properties for feedback data, such as healthcare-related feedback data, where the provided solutions can be integrated in existing feedback processing systems without the need to modify underlying feedback gathering and processing mechanisms. Accordingly, by disclosing solutions for jointly detecting topic and sentiment designations for feedback data, various embodiments of the present invention address technical challenges related to accurately analyzing sentiment information about feedback data to detect relevant semantic properties for such feedback data.

IV. EXEMPLARY SYSTEM OPERATION

Various embodiments of the present invention address technical challenges related to accurately and efficiently detecting reliable properties for relatively short NLP input data, including feedback data associated many healthcare systems, by utilizing NLP models that detect important features from NLP data using groups of interconnected factorization-based optimizations. For example, various embodiments of the present invention use optimization of non-negative matrix factorization (NMF) models and/or non-negative matrix tri-factorization (3-factor NMF) models. By utilizing NMF models and/or 3-factor NMF models, various embodiments of the present invention effectively and efficiently relate raw feature data associated with NLP data to more sophisticated correlation models that describe semantically deeper inferences about the NLP data. In doing so, various embodiments of the present invention address technical drawbacks of many existing NLP systems in accurately and efficiently detecting reliable properties for relatively short NLP input data, including feedback data associated many healthcare systems.

Joint Topic-Sentiment Detection

FIG. 4is a data flow diagram of an example process400for generating a topic detection403and a sentiment detection404for each of one or more unlabeled NLP input data items402. Via the various steps/operations of process400, a system of one or more computers (e.g., the NLP system101ofFIG. 1) can perform joint topic-sentiment detection for NLP inputs.

The process400begins when the NLP computing entity106receives the one or more unlabeled NLP input data items402from the unlabeled data storage unit122and one or more labeled NLP input data items401from the labeled data storage unit121. In some embodiments, a labeled NLP input data item401is a collection of data (e.g., a collection of text data, such as a digital document) that is associated with a pre-existing sentiment label (where the pre-existing sentiment label for the labeled NLP input data item401is part of the labeled NLP input data item401), while an unlabeled NLP input data item401is a collection of data that is not associated with a preexisting sentiment label. In some embodiments, the NLP computing entity106is configured to use the labeled NLP input data items401and the unlabeled NLP input data items402(collectively, the NLP input data items405) to determine at least one of the topic detection403and the sentiment detection404for each of the unlabeled NLP input data items402. In some embodiments, the storage system108is configured to retrieve at least some of the unlabeled NLP input data items402and/or at least some of labeled NLP input data items401from one or more external computing entities102.

The process400continues when the NLP computing entity106uses the NLP input data items405to determine the topic detection403and the sentiment detection404for each of the unlabeled NLP input data items402. In some embodiments, when each NLP input data item in the NLP input data items405is associated with a digital document (e.g., a collection of data such as text, including a collection feedback data), the NLP computing entity106may utilize the NLP input data items405to generate document-topic correlation data and document-sentiment correlation data for the NLP input data items. The NLP computing entity106may then utilize the document-topic correlation data and the document-sentiment correlation data for the NLP input data items to generate the topic detection403and the sentiment detection404for each of the unlabeled NLP input data items402.

The document-topic correlation data may include one or more document-topic correlation objects (e.g., a document-topic correlation matrix) that indicate, for each digital document associated with the NLP input data items405, a corresponding document-topic correlation indicator for the respective digital document and each of one or more topics. In some embodiments, a document-topic correlation indicator for a digital document and a topic indicates a measure of correspondence of the digital document with the topic. In some embodiments, a topic may be defined by one or more terms and/or one or more weight values that each relate the topic to a particular. For example, a particular topic associated with law may be characterized by words such as “law, legal, lawyer, jury, case.” As another example, a particular topic associated with sports may be characterized by weight values such as weight(“sports”)=1.0, weight(“athlete”)=0.9, weight(“match”)=0.5, and weight(“trophy”)=0.4.

The document-sentiment correlation data may include one or more document-sentiment correlation objects (e.g., a document-sentiment correlation matrix) that indicate, for each digital document associated with the NLP input data items405, a corresponding document-sentiment correlation indicator for the digital document and each of one or more sentiments. In some embodiments, a document-sentiment correlation indicator for a digital document and a sentiment indicates a measure of correspondence of the digital document with the sentiment. In some embodiments, a sentiment may be characterized by one or more discrete and/or one or more continuous values, where the noted values may be selected from a sentiment space such as an embedding space. Examples of sentiments may include a happy sentiment, an angry sentiment, a concerned sentiment, other sentiments, and/or the like. Sentiments may be defined by one-dimensional and/or multi-dimensional values.

In some embodiments, the document-topic correlation data503and the document-sentiment correlation data504for the NLP input data items405may be computed using the process500ofFIG. 5. As depicted inFIG. 5, process500begins when the term correlation modeling engine111processes the NLP input data items405to generate term correlation data501for the NLP input data items405. In some embodiments, the term correlation data501comprises one or more term correlation data objects (e.g., a term correlation matrix) that indicate, for each term of a group of terms, a corresponding measure of co-occurrence likelihood of the respective term with each other term in the group of terms, where the measure of co-occurrence frequency is determined based on co-occurrences of terms in the NLP input data items405(e.g., in one or more digital documents associated with the NLP input data items405).

In some embodiments, the term correlation modeling engine111generates the term correlation data501in accordance with process600ofFIG. 6. As depicted inFIG. 6, process600begins at step/operation601when the term correlation modeling engine111obtains the NLP input data items405. At step/operation602, the term correlation modeling engine111performs pre-processing on the NLP input data items405to generate pre-processed NLP input data items. Examples of pre-processing tasks performed on the NLP input data items405to generate the pre-processed NLP input data items may include shallow NLP tasks such as chunking, shallow parsing, tokenizing, and/or the like.

At step/operation603, the term correlation modeling engine111generates an n-gram model for the pre-processed NLP input data items, where the n-gram model may identify one or more n-grams (e.g., unigrams, bigrams, where n may be determined by system configuration data) in the pre-processed NLP input data items. An n-gram may be a combination of one or more words and/or semantic tokens. In this disclosure, the terms “n-gram” and “term” have been used interchangeably. In some embodiments, to generate the n-gram model for the pre-processed NLP input data items, the term correlation modeling engine111may utilize one or more syntactic indicators of n-grams (e.g., whitespace characters). In some embodiments, to generate the n-gram model for the pre-processed NLP input data items, the term correlation modeling engine111may utilize a sematic model of the pre-processed NLP input data items, such as a semantic model determined using part-of-speech tagging.

At step/operation604, the term correlation modeling engine111generates term occurrence probability data for one or more selected terms, where the one or more selected terms may include at least some of the terms identified by the n-gram model. In some embodiments, the term occurrence probability data for the selected terms include: (i) a singular occurrence probability for each selected term and (ii) a corresponding joint occurrence probability for each pair of selected terms. For example, given three selected terms ti, tj, and tk, the term correlation modeling engine111may generate a singular occurrence probability P(ti) for the term ti, a singular occurrence probability P(tj) for the term tj, a singular occurrence probability P(tk) for the term tk, a joint occurrence probability P(ti,tj) for the terms tiand tj, a joint occurrence probability P(ti,tk) for the terms tiand tk, and a joint occurrence probability P(tj, tk) for the terms tjand tk.

In some embodiments, to generate a singular term distribution probability P(tn) for a term tn, the term correlation modeling engine111performs operations corresponding to the equation:

P⁡(tn)=∑m⁢#⁢(tn,tm)∑p,q⁢#⁢(tp,tq),(Equation⁢⁢1)
where #(ta,tb) denotes a measure of frequency of co-occurrence of terms taand tbin the preprocessed NLP data (e.g., occurrence of the two terms within the same digital document and/or within a threshold proximity of each other). In some embodiments, to generate a joint term distribution probability P(tn,tm) for the terms tnand tm, the term correlation modeling engine111performs operations corresponding to the equation:

P⁡(tn,tm)=#⁢⁢(tm,tn)∑p,q⁢#⁢(tp,tq),(Equation⁢⁢2)
where #(ta, tb) denotes a measure of frequency of co-occurrence of terms taand tbin the preprocessed NLP data.

At step/operation605, the term correlation modeling engine111generates the term correlation data501for the selected terms based on the term occurrence probability data for the selected terms. In some embodiments, the term correlation data501include, for each pair of selected terms, a term correlation indicator. In some embodiments, the term correlation indicator for a pair of selected terms indicates a measure of co-occurrence frequency of the pair of selected terms in the NLP input data items405. In some embodiments, the term correlation indicator for a pair of selected terms is determined based on a point-wise mutual information (PMI) indicator for the pair of selected terms. In some embodiments, to determine a term correlation indicator rijfor a pair of terms tiand tj, the term correlation modeling engine111performs operations corresponding to the equation:

Returning toFIG. 5, after generating the term correlation data501, the term correlation modeling engine111provides the term correlation data501to the term-topic modeling engine112, which in turn uses the term correlation data501to generate term-topic correlation data502. In some embodiments, the term topic data may include one or more term-topic correlation objects (e.g., a term-topic correlation matrix) that indicate, for each of one or more selected terms associated with the NLP input data items405(e.g., the selected terms identified based on the n-gram model generated in step/operation603), a corresponding term-topic correlation indicator for the respective selected term and each of one or more topics. In some embodiments, to determine a term-topic correlation matrix U based on a term correlation matrix R (e.g., a matrix that may include term correlation data for the selected terms), the term-topic modeling engine112optimizes the following cost function:
J1(U)=½∥R−UUT∥2(Equation 4)
where T denotes a matrix transpose operation and term-topic correlation data U is greater than or equal to zero.

In some embodiments, a term-topic correlation indicator for a selected term and a topic indicates a measure of correspondence of the selected term with the topic. In some embodiments, a topic may be defined by one or more terms and/or one or more weight values that each relate the topic to a particular. For example, a particular topic associated with law may be characterized by words such as “law, legal, lawyer, jury, case.” As another example, a particular topic associated with sports may be characterized by weight values such as weight(“sports”)=1.0, weight(“athlete”)=0.9, weight(“match”)=0.5, and weight(“trophy”)=0.4.

After generating the term-topic correlation data502, the term-topic modeling engine112provides the term-topic correlation data502to both the topic detection engine113and the sentiment detection engine114. The topic detection engine113uses the term-topic correlation data502to generate the document-topic correlation data503, while the sentiment detection engine114uses the term-topic correlation data502to generate the document-sentiment correlation data504. In some embodiments, the topic detection engine113determines the document-topic correlation data503based on the term-topic correlation data502using an NMF optimization of the document-topic correlation data503into two or more optimization factors, where the two or more optimization factors may include the term-topic correlation data502. In some embodiments, the sentiment detection engine114determines the document-topic correlation data503based on the term-topic correlation data502using a 3-factor NMF into three or more optimization factors, where the three or more optimization factors may include the term-topic correlation data502.

In some embodiments, to generate the document-topic correlation data503based on the term-topic correlation data502, the topic detection engine113performs the various steps/operations of process700ofFIG. 7. The process700starts at step/operation701when the topic detection engine113obtains the term correlation data501. At step/operation702, the topic detection engine113obtains the term-document correlation data. In some embodiments, the term-document correlation data may include one or more term-document correlation objects (e.g., a term-document correlation matrix) that indicate, for each selected term of one or more selected terms associated with the NLP input data items405(e.g., the selected terms identified based on the n-gram model generated in step/operation603), a term-document correlation indicator for each of one or more digital documents associated with the NLP input data items405. In some embodiments, a term-document correlation indicator for a selected term and a digital document is a measure of presence or absence of the selected term in the digital document and/or a measure of extent of occurrence of the selected term in the digital document. At step/operation703, the topic detection engine113generates the document-topic correlation data503based on the term correlation data501and the term-document correlation data. In some embodiments, to generate the document-topic correlation data V based on the term correlation data U and the term-document correlation data V, the topic detection engine113optimizes the following NMF equation:
J2(V)=½∥X−UVT∥2(Equation 5)
where T denotes a matrix transpose operation and term-document correlation data V has to be more than or equal to zero.

In some embodiments, to generate the document-sentiment correlation data504based on the term-topic correlation data502, the sentiment detection engine114performs the various steps/operations of process800ofFIG. 8. The process800begins at step/operation801when the sentiment detection engine114obtains term-topic correlation data502. The process begins at step/operation801when the sentiment detection engine114obtains the term correlation data501. At step/operation802, the sentiment detection engine114obtains term-document correlation data (e.g., the term-document correlation data discussed in reference to step/operation702ofFIG. 7).

At step/operation803, the sentiment detection engine114obtains prior sentiment label data for the NLP input data items405. In some embodiments, the prior sentiment label data are determined based on the labeled NLP input data items401. In some embodiments, the prior sentiment label data are stored in one or more prior sentiment label data objects (e.g., a prior sentiment label matrix) that indicates: (i) for each of one or more labeled NLP input data items401, a prior sentiment label, and (ii) for each of the one or more unlabeled NLP input data items402, designated prior sentiment labels (e.g., null prior sentiment labels).

At step/operation804, the sentiment detection engine114obtains term-sentiment correlation data. In some embodiments, the term-sentiment correlation data may include one or more term-sentiment correlation objects (e.g., a term-sentiment correlation matrix) that indicate, for each selected term of one or more selected terms associated with the NLP input data items405(e.g., the selected terms identified based on the n-gram model generated in step/operation603), a term-sentiment correlation indicator for each of one or more sentiments. In some embodiments, a term-sentiment correlation indicator for a selected term and a sentiment is a measure of correspondence of the selected term to the sentiment.

At step/operation805, the sentiment detection engine114generates the document-sentiment correlation data504based on the term-topic correlation data502, the term-document correlation data, the prior sentiment label data, and the topic-sentiment correlation data. In some embodiments, to the document-sentiment correlation data W based on the term-topic correlation data U, the term-document correlation data X, the prior sentiment label data W0, and the term-sentiment correlation data S, the sentiment detection engine114performs operations corresponding to the following 3-factor NMF equation:
min ½[∥X−USWT∥2+μ(tr(W−W0)TC(W−W0))],  (Equation 6)
where tr is a matrix trace operation, μ is a positive scaling factor, T denotes a matrix transpose operation, and C is a diagonal matrix whose diagonal values are set to a designated value (e.g., a one value).

Returning toFIG. 5, at least one of the generation of the term-topic correlation data502by the term-topic modeling engine112, generation of the document-topic correlation data503by the topic detection engine113, and generation of document-sentiment correlation data504by the sentiment detection engine114may be performed using a factorization-based optimization. Examples of parameters for such matrix-factorization-based optimizations are depicted in the process900ofFIG. 9. As depicted inFIG. 9, the process900may include a first factorization-based optimization921which factorizes a term correlation matrix R901into a term-topic correlation matrix U902and a term-topic correlation transpose matrix UT903. Furthermore, the process900may include a second factorization-based optimization922which factorizes a term-document correlation matrix X904into the term-topic correlation matrix U902and a document-topic correlation transpose matrix VT905. Moreover, the process900may include a third factorization-based optimization923which factorizes the term-document correlation matrix X904into the term-topic correlation matrix U902, a topic-sentiment correlation matrix S906, and a prior sentiment label transpose matrix WT907.

Returning toFIG. 4, the NLP computing entity106may determine the topic detection403based on the document-topic correlation data503. In some embodiments, the NLP computing entity106may determine, for each digital document associated with the NLP input data items405, n topics having the highest document-topic correlation indicator as the detected topics for the digital document. In some embodiments, the NLP computing entity106may determine, for each digital document associated with the NLP input data items405, any topics whose document-topic correlation indicator in relation to the digital document exceeds a threshold document-topic correlation as the detected topics for the digital document.

The NLP computing entity106may also determine the sentiment detection404based on the document-sentiment correlation data504. In some embodiments, the NLP computing entity106may determine, for each digital document associated with the NLP input data items405, n sentiments having the highest document-sentiment correlation indicator as the detected sentiment for the digital document. In some embodiments, the NLP computing entity106may determine, for each digital document associated with the NLP input data items405, any sentiments whose document-sentiment correlation indicator in relation to the digital document exceeds a threshold document-sentiment correlation as the detected sentiment for the digital document.

Feedback Processing

At least some aspects of the joint topic-sentiment detection for NLP input data items may be utilized by a feedback processing system which analyzes NLP feedback data to determine operational conclusions for a feedback-receiving institution. An example of NLP feedback data may include feedback data associated with a healthcare provider institution and/or a health insurance provider system, such as survey response data received from providers and/or patients. In some embodiments, at least some aspects of the joint topic-sentiment detection for NLP input data items may be utilized as part of provider listening post synthesized analysis by a health insurance provider institution during a credentialing phase of a medical claim lifecycle.

FIG. 10is an operational flow diagram of an example process1000for processing incident feedback data for an incident. Via the various steps/operations of process400, a system of one or more computers (e.g., the NLP system101ofFIG. 1) can perform feedback processing by using joint topic-sentiment detection of NLP inputs.

The process1000begins when the NLP computing entity106obtains incident data1001and the incident feedback data1002for the incident. The NLP computing entity106processes the incident data1001and the incident feedback data1002to determine an NPS1003for the incident feedback data1002, where the NPS1003is an example of a customer behavior indicator at a brand level for the incident feedback data1002. The NLP computing entity106further processes the incident feedback data1002, along with labeled feedback data (e.g., the labeled NLP input data items401), to determine a sentiment detection1005(e.g., the sentiment detection404) for the incident feedback data1002. The NLP computing entity106further processes the sentiment detection1005for the incident feedback data1002to determine a topic detection1008(e.g., the topic detection403) for the incident feedback data1002.

At step/operation1006, the NLP computing entity106determines whether the incident feedback data1002relates to critical feedback1009and/or to positive feedback. For example, the NLP computing entity106may determine that the incident feedback data1002relates to critical feedback1009if the sentiment detection1005for the incident feedback data1002exceeds a sentiment detection threshold and/or if the NPS1003for the incident feedback data1002exceeds an NPS threshold. As another example, the NLP computing entity106may determine that the incident feedback data1002relates to positive feedback if the sentiment detection1005for the incident feedback data1002fails to exceed a sentiment detection threshold and/or if the NPS1003for the incident feedback data1002fails to exceed an NPS threshold. In some embodiments, the NLP computing entity106may determine that the incident feedback data1002relates to critical feedback1009if the sentiment detection1005for the incident feedback data1002exceeds a sentiment detection threshold and the NPS1003for the incident feedback data1002exceeds an NPS threshold. In some embodiments, the NLP computing entity106may determine that the incident feedback data1002relates to positive feedback if either the sentiment detection1005for the incident feedback data1002exceeds a sentiment detection threshold or the NPS1003for the incident feedback data1002exceeds an NPS threshold.

Process1000continues with the NLP computing entity106storing the incident feedback data in a best practice database1007in response to determining that the incident feedback data1002relates to positive feedback. Moreover, in response to determining that the incident feedback data1002relates to critical feedback1009, the NLP computing entity106determines critical topics1010(e.g., indicating which particular detected topics associated with the topic detection1008pertain to the critical sentiment of the incident feedback data1002) and/or critical sub-topics1011(e.g., indicating which particular detected sub-topics associated with the detected critical topics1010pertain to the critical sentiment of the incident feedback data1002). The NLP computing entity106then stores the detected critical topics1010and/or the detected critical sub-topics1011in the critical practice database1012.

In some embodiments, the NLP computing entity106processes unlabeled feedback digital documents to generate, for each unlabeled feedback document, a sentiment detection404and a topic detection403. For example, the NLP computing entity106may determine that a first unlabeled feedback document has a positive sentiment and relates to inpatient services. In response, the NLP computing entity106may designate the topic detection403for the first unlabeled feedback document (e.g., the inpatient services topic) as a positively-reflected topic. As another example, the NLP computing entity106may determine that a second unlabeled feedback document has a positive sentiment and relates to outpatient services. In response, the NLP computing entity106may designate the topic detection403for the second unlabeled feedback document (e.g., the outpatient services topic) as a positively-reflected topic.

As a further example, the NLP computing entity106may designate the sentiment detection404for a third unlabeled feedback document as a negative sentiment detection. In response, the NLP computing entity106may determine a topic detection404and/or a sub-topic detection associated with the third unlabeled feedback document as a critical topic and/or sub-topic. In some embodiments, the NLP computing entity106may utilize one or more critical topics and/or one or more critical sub-topics to generate an incident report. In some embodiments, the NLP computing entity106may utilize one or more critical topics and/or one or more critical sub-topics to generate one or more real-time alerts, such as one or more voice prompt alerts. In some embodiments, the NLP computing entity106may utilize one or more critical topics and/or one or more critical sub-topics to seek additional feedback from interested user profiles and/or user profiles deemed to be informed about the one or more critical topics and/or the one or more critical sub-topics.