Abstract: A method and a system for model training are provided. The method can include training a first classifier, a second classifier, and a third classifier with subsets of a labeled dataset. The method can also include predicting a pseudo labeled dataset from an unlabeled dataset using the first classifier, the second classifier, and the third classifier. The method further includes assigning a role to the first classifier, to the second classifier, and to the third classifier. The method can further include selecting a teaching sample dataset from the pseudo labeled dataset based on the role assigned to the third classifier, wherein the third classifier is assigned a role of a student. The method can also include retraining the third classifier with the teaching sample dataset in conjunction with a subset of the labeled dataset.

Abstract: A cognitive system (artificial intelligence) is optimized by assessing different data augmentation methods used to augment training data, and then training the system using a training set augmented by the best identified method. The augmentation methods are assessed by applying them to the same set of training data to generate different augmented training data sets. Respective instances of the cognitive system are trained with the augmented sets, and each instance is subjected to validation testing to assess its goodness. The validation testing can include multiple validation tests leading to component scores, and a combined validation score is computed as a weighted average of the component scores using respective weights for each validation test. The augmentation method corresponding to the instance having the highest combined validation score is selected as the optimum augmentation method for the particular cognitive system at hand.

Abstract: A cognitive system (artificial intelligence) is optimized by assessing different data augmentation methods used to augment training data, and then training the system using a training set augmented by the best identified method. The augmentation methods are assessed by applying them to the same set of training data to generate different augmented training data sets. Respective instances of the cognitive system are trained with the augmented sets, and each instance is subjected to validation testing to assess its goodness. The validation testing can include multiple validation tests leading to component scores, and a combined validation score is computed as a weighted average of the component scores using respective weights for each validation test. The augmentation method corresponding to the instance having the highest combined validation score is selected as the optimum augmentation method for the particular cognitive system at hand.

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: 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: 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: 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: Tracking of brand followers utilizing social media history and generation of directed advertising. A computing device receives a brand for market analysis, the brand having a brand account on a social media platform. Social media history is received regarding social media habits of social media users. A distribution of brand followers is determined among social media users following the brand account. One or more clusters are generated for viewing market segmentation regarding the brand for market analysis. Brand data is received regarding a plurality of brands having brand accounts on the social media platform, follower profiles for brand followers of the plurality of brand accounts, and social media information for the followers of the plurality of brand accounts. An influencer analysis is generated. Historical follower profiles are received. A historical distribution analysis is generated. A market analysis is generated. Directed advertising is generated based upon the market analysis.

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: 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: 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: 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: 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.