Abstract: A method, system and a computer program product are provided for generating a natural language model that is substantially independent of languages and domains by transforming monolingual embeddings into a multilingual embeddings in a first shared embedding space using a cross-lingual learning process, and then transforming the multilingual embeddings into cross-domain, multilingual embeddings in a second shared embedding space using a cross-domain learning process, where the multilingual embeddings and/or cross-domain, multilingual embeddings are evaluated to measure a degree to which the embeddings associate a set of target concepts with a set of attribute words.

Abstract: A system configured to predict fine-grained affective states. The system comprising a processor configured to execute instructions to create training data comprising content conveying emotions, and to create a trained model by performing an emotion vector space model training process using the training data to train a model using a feed forward neural network that converts discrete emotions into emotion vector representations. The system uses the trained model to predict fine-grained affective states for text conveying an emotion.

Abstract: A computer-implemented method for fine-grained affective states prediction. The computer-implemented method creates training data comprising content conveying emotions. The method creates a trained model by performing an emotion vector space model training process using the training data to train a model using a feed forward neural network that converts discrete emotions into emotion vector representations. The trained model can be used to predict fine-grained affective states for text conveying an emotion.

Abstract: A computer-implemented method according to one embodiment includes identifying a plurality of communications each having an associated objective, automatically determining a plurality of dialog acts within the plurality of communications, utilizing a classifier, determining a plurality of features within each of the plurality of communications, training the model, utilizing the plurality of communications and associated objectives, the plurality of dialog acts, and the plurality of features, receiving textual data associated with a real-time conversation between a first participant and a second participant, receiving an objective of the first participant for the real-time conversation between the first participant and the second participant, determining a dialog act to be entered by the first participant at a current point in the real-time conversation that meets the objective, utilizing the model, and determining the dialog act to be entered, based on the scoring, and returning the dialog act to the first pa

Abstract: Mechanisms are provided to implement a brand comparison engine. The mechanisms receive a request to compare brand personalities of a first specified brand and a second specified brand and obtain a first brand personality scale associated with the first specified brand and a second brand personality scale associated with the second specified brand. The mechanisms calculate at least one gap value indicating a difference between at least one personality trait in the first brand personality scale and a corresponding at least one personality trait in the second brand personality scale. The mechanisms also output an output indicating an aspect of the at least one gap based on the calculation.

Abstract: A computer-implemented method according to one embodiment includes receiving, utilizing a processor, textual data associated with a conversation between a first participant and a second participant, receiving, utilizing the processor, an objective of the first participant for the conversation between the first participant and the second participant, receiving, utilizing the processor, a proposed dialog act to be entered in the conversation from the first participant, determining, utilizing the processor, an impact of the proposed dialog act on the objective of the first participant, utilizing a model, and performing, utilizing the processor, one or more actions based on the impact of the proposed dialog act.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a personalized interactive decision support system. A personalized product recommendation module executing within the personalized interactive decision support system correlates at least one customer to a set of consumption preferences using a machine learning model based on a set of traits of the at least one customer to form at least one customer-to-preference correlation. The personalized product recommendation module maps a set of products to the set of consumption preferences using a consumption preferences-to-product attribute mapping data structure based on a set of attributes of the set of products to form a set of product-to-preference correlations.

Abstract: A method, a computer program product, and an information handling system is provided for reducing cost and improving real-time performance for analyzing and representing customer care communication progression based on tone assessments by monitoring communications between a plurality of customers and a plurality of representatives to form a plurality of interaction histories. Analyzing a text form of the plurality of interaction histories according to a Tone Latent Dirichlet Allocation [T-LDA] model to determine a plurality of conversation tones. Mapping the plurality of conversation tones to a plurality of conversation phases and providing a representation of the conversation tones identifying a first customer tone at a first conversation phase and a second customer tone at a second conversation phase.

Abstract: An approach is provided in which a system determines a set of message properties corresponding to a set of products based on product data analysis. The system then identifies a set of user properties of users based on analyzing social media data corresponding to the product data. Next, the system identifies a set of candidate customers from the set of users based on analyzing the set of user properties against the set of product data. In turn, the system generates a set of target messages that are tailored to a combination of candidate customer properties corresponding to the set of candidate customers and the product data.

Abstract: Mechanisms are provided to implement a brand comparison engine. The mechanisms receive a request to compare brand personalities of a first specified brand and a second specified brand and obtain a first brand personality scale associated with the first specified brand and a second brand personality scale associated with the second specified brand. The mechanisms calculate at least one gap value indicating a difference between at least one personality trait in the first brand personality scale and a corresponding at least one personality trait in the second brand personality scale. The mechanisms also output an output indicating an aspect of the at least one gap based on the calculation.

Abstract: Input of a conversation is received. The conversation includes at least a first user. An utterance of the conversation is analyzed to identify a dialog act attribute, an emotion attribute, and a tone attribute. The dialog act attribute, emotion attribute, and tone attribute are annotated to the utterance of the conversation. The conversation is validated based on the annotated attributes compared with a threshold. The annotated conversation and the validation of the conversation are stored.

Abstract: A method, a computer program product, and an information handling system is provided for labeling unlabeled utterances given a taxonomy of labels utilizing topic word semi-supervised learning.

Abstract: A computer-implemented method according to one embodiment includes receiving, utilizing a processor, textual data associated with a real-time conversation between a first participant and a second participant; receiving, utilizing the processor, an objective of the first participant for the real-time conversation between the first participant and the second participant; determining, utilizing the processor, a dialog act to be entered by the first participant at a current point in the real-time conversation that meets the objective, utilizing a model, including scoring a plurality of proposed dialog acts based on an amount that each proposed dialog act will change a probability of the objective being achieved during a current point in the real-time conversation, and determining the dialog act to be entered, based on the scoring; and returning, utilizing the processor, the dialog act to the first participant.

Abstract: A method, a computer program product, and an information handling system is provided for reducing cost and improving real-time performance for analyzing and representing customer care communication progression based on tone assessments by monitoring communications between a plurality of customers and a plurality of representatives to form a plurality of interaction histories. Analyzing a text form of the plurality of interaction histories according to a Tone Latent Dirichlet Allocation [T-LDA] model to determine a plurality of conversation tones. Mapping the plurality of conversation tones to a plurality of conversation phases and providing a representation of the conversation tones identifying a first customer tone at a first conversation phase and a second customer tone at a second conversation phase.

Abstract: Input of a conversation is received. The conversation includes at least a first user. An utterance of the conversation is analyzed to identify a dialog act attribute, an emotion attribute, and a tone attribute. The dialog act attribute, emotion attribute, and tone attribute are annotated to the utterance of the conversation. The conversation is validated based on the annotated attributes compared with a threshold. The annotated conversation and the validation of the conversation are stored.

Abstract: A computer-implemented method according to one embodiment includes receiving, utilizing a processor, textual data associated with a real-time conversation between a first participant and a second participant, receiving, utilizing the processor, an objective of the first participant for the real-time conversation between the first participant and the second participant, determining, utilizing the processor, a dialog act to be entered by the first participant at a current point in the real-time conversation that meets the objective, utilizing a model, and returning, utilizing the processor, the dialog act to the first participant.

Abstract: Mechanisms are provided to implement a brand personality perception gap assessment engine. The mechanisms receive an inferred brand personality for a specified brand. The mechanisms further receive an intended brand personality for the specified brand. The mechanisms calculate at least one gap between the inferred brand personality and the intended brand personality. The mechanisms output an output indicating aspects of the at least one gap.

Abstract: Mechanisms are provided to implement a brand comparison engine. The mechanisms receive a request to compare brand personalities of a first specified brand and a second specified brand and obtain a first brand personality scale associated with the first specified brand and a second brand personality scale associated with the second specified brand. The mechanisms calculate at least one gap value indicating a difference between at least one personality trait in the first brand personality scale and a corresponding at least one personality trait in the second brand personality scale. The mechanisms also output an output indicating an aspect of the at least one gap based on the calculation.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a personalized interactive decision support system. A customer profile generator executing within the personalized interactive decision support system determines a set of traits for at least one customer based on at least one user-generated content source. A personalized product recommendation module executing within the personalized interactive decision support system matches the at least one customer to at least one product within a set of products based on a consumption preferences-to-product attribute mapping data structure to form at least one product recommendation.

Abstract: In an approach for training customer service agents using persona-based chatbots, a processor retrieves customer service interaction information. A processor analyzes, using natural language processing, the customer service interaction information, wherein the analyzing includes preprocessing and aggregating the customer service interaction information. A processor interacts with a user. A processor provides feedback to the user, based on the user's style and performance during training.