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: The invention relates to identifying and evaluating target coding sequences for control of plant parasitic nematodes by inhibiting one or more biological functions, and their use. The invention provides methods and compositions for identification of such sequences and for the control of a plant-parasitic nematode population. By feeding one or more recombinant double stranded RNA molecules provided by the invention to the nematode, a reduction in disease may be obtained through suppression of nematode gene expression. The invention is also directed to methods for making transgenic plants that express the double stranded RNA molecules, and the plant cells and plants obtained thereby.

Abstract: A text stream having one or more sentences is received, and any number of the one or more sentences are parsed to determine corresponding subject-verb-object (SVO) triples. Each sentence whose corresponding SVO triple contains an identified verb is selected, based on the identified verb, or a lemma of the identified verb, matching a predefined verb. A subject of each selected sentence is identified as an aspect candidate. Each identified aspect candidate is tokenized and normalized. One or more n-grams are generated for each tokenized and normalized aspect candidate. For each generated n-gram, a frequency at which the n-gram is generated is determined. A number of the generated n-grams are selected as aspects based on the frequency with which the number of n-grams are generated.

Abstract: An embodiment provides methods and arrangements for visualizing and exploring natural-language text. In an embodiment, natural language text is received, and this is parsed to form a directed graph comprising a plurality of nodes. The directed graph is converted to an outline graph comprising core nodes and layer nodes. The outline graph is simplified, and there is created, for display on a user interface, an interactive visual representation of the simplified outline graph. Other variants and embodiments are broadly contemplated herein.

Abstract: Embodiments relate to relationship modeling and visualization from social media. One aspect includes determining a relationship type of a network-based relationship, between an individual and a network contact of the individual, from at least one social media data source. The relationship type is determined using a relationship model based on relationship types that include operational, personal, and business. Another aspect includes performing timeline based relationship strength segmentation using Group Lasso. The timeline based relationship strength segmentation specifies a past and current strength of the relationship. A further aspect includes predicting a future strength of the relationship using Extended Kalman Filter, and providing, through a visual interface, interactive visual analytics to view and monitor relationship states including the past, current, and future strengths over time.

Abstract: An approach is provided that provides a tone optimization recommendation. The approach obtains a current tone inferred from digital content and a desired tone inference for a target audience. A tone optimization recommendation to reduce a difference between the current tone and the desired tone is determined using a processor. A memory is modified to save the tone optimization recommendation. The tone optimization recommendation is provided.

Abstract: An embodiment provides methods and arrangements for visualizing and exploring natural-language text. In an embodiment, natural language text is received, and this is parsed to form a directed graph comprising a plurality of nodes. The directed graph is converted to an outline graph comprising core nodes and layer nodes. The outline graph is simplified, and there is created, for display on a user interface, an interactive visual representation of the simplified outline graph. Other variants and embodiments are broadly contemplated herein.

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: Mechanisms are provided to implement a brand personality inference engine. The mechanisms receive crowdsource information and extract features associated with a brand from the crowdsource information. The crowdsource information comprises natural language content submitted by a plurality of providers to a crowdsource information source. The mechanisms analyze features associated with the brand in accordance with a brand personality model configured to predict a brand personality for the brand based on the features associated with the brand. The mechanisms generate an inferred brand personality data structure, representing a perceived brand personality of providers providing the crowdsource information, and output an output indicating aspects of the perceived brand personality based on the inferred brand personality data structure.

Abstract: Embodiments relate to interacting with a collection of user opinion documents associated with a topic. One aspect includes obtaining opinion data for the collection of opinion documents associated with the topic. The opinion data includes one or more features discussed in the opinion documents, one or more key phrases included in each feature, one or more text snippets included in each feature, and at least one sentiment expressed in each text snippet. A visual interface is provided in which a feature summary view of the opinion documents acts a top level of a navigational hierarchy. The visual interface allows user navigation from the top level to a lower level of the navigational hierarchy in order to display more details about a text snippet, as compared to the feature summary view, while continuing to provide the feature summary view.

Abstract: A text stream having one or more sentences is received, and any number of the one or more sentences are parsed to determine corresponding subject-verb-object (SVO) triples. Each sentence whose corresponding SVO triple contains an identified verb is selected, based on the identified verb, or a lemma of the identified verb, matching a predefined verb. A subject of each selected sentence is identified as an aspect candidate. Each identified aspect candidate is tokenized and normalized. One or more n-grams are generated for each tokenized and normalized aspect candidate. For each generated n-gram, a frequency at which the n-gram is generated is determined. A number of the generated n-grams are selected as aspects based on the frequency with which the number of n-grams are generated.

Abstract: Brands and users are matched by determining their personalities and pairing them based on user-brand associations. Modeling parameters are estimated based on the personalities of each pair. Additional pairs of users and brands having unknown associations are identified, and each such additional pair's correlation, which may be a score, is determined, based on the modelling parameters. Brands and users having unknown associations are matched based on their correlations scores matching one or more criteria.

Abstract: Embodiments relate to relationship modeling and visualization from social media. One aspect includes determining a relationship type of a network-based relationship, between an individual and a network contact of the individual, from at least one social media data source. The relationship type is determined using a relationship model based on relationship types that include operational, personal, and business. Another aspect includes performing timeline based relationship strength segmentation using Group Lasso. The timeline based relationship strength segmentation specifies a past and current strength of the relationship. A further aspect includes predicting a future strength of the relationship using Extended Kalman Filter, and providing, through a visual interface, interactive visual analytics to view and monitor relationship states including the past, current, and future strengths over time.

Abstract: A text stream having one or more sentences is received, and any number of the one or more sentences are parsed to determine corresponding subject-verb-object (SVO) triples. Each sentence whose corresponding SVO triple contains an identified verb is selected, based on the identified verb, or a lemma of the identified verb, matching a predefined verb. A subject of each selected sentence is identified as an aspect candidate. Each identified aspect candidate is tokenized and normalized. One or more n-grams are generated for each tokenized and normalized aspect candidate. For each generated n-gram, a frequency at which the n-gram is generated is determined. A number of the generated n-grams are selected as aspects based on the frequency with which the number of n-grams are generated.

Abstract: Embodiments relate to monitoring personal emotion states over time from social media. One aspect includes extracting personal emotion states from at least one social media data source using a semantic model including an integration of numeric emotion measurements and semantic categories. Timeline based emotion segmentation with consistent emotional semantics is performed based on the semantic model. In a visual interface, interactive visual analytics are provided to explore and monitor personal emotional states over time including both a numeric and semantic interpretation of emotions with visual encodings. Visual evidence for analytical reasoning of emotion is also provided.

Abstract: Embodiments relate to interacting with a collection of user opinion documents associated with a topic. One aspect includes obtaining opinion data for the collection of opinion documents associated with the topic. The opinion data includes one or more features discussed in the opinion documents, one or more key phrases included in each feature, one or more text snippets included in each feature, and at least one sentiment expressed in each text snippet. A visual interface is provided in which a feature summary view of the opinion documents acts a top level of a navigational hierarchy. The visual interface allows user navigation from the top level to a lower level of the navigational hierarchy in order to display more details about a text snippet, as compared to the feature summary view, while continuing to provide the feature summary view.