Detecting emotional events in textual content

A computer system for determining resource dispositions for identifying key emotional content in a text, the computer system comprising:

• a memory;
• and
• one or more processors in communication with the memory, wherein the computer system is configured to perform a method, said method comprising:
• obtaining, by the one or more processors, the text from a computing resource in a domain;
• segmenting, by the one or more processors, the text into sentences;
• utilizing, by the one or more processors, pre-defined entities for the domain, to identify entities in each sentence of the sentences comprising the text;
• classifying, by the one or more processors, each entity of the identified entities with a sentiment and extracting the sentiments, wherein the classifying assigns a polarity to each sentiment of the sentiments;
• identifying, by the one or more processors, for each sentence, a given entity with a strongest sentiment, wherein the identifying comprises quantitively determining a value for each of the sentiments and utilizing the values to identify the strongest sentiment;
• deriving, by the one or more processors, for each sentence, for each entity, one or more emotions and classifying each emotion of the one or more emotions, wherein the classifying comprising:
• assigning a polarity to each emotion;
• and
• associating a score with each emotion to reflect a strength of each emotion within the polarity;
• calculating, by the one or more processors, for the one or more emotions in each sentence of the text, based on the scores, a mean and a standard deviation from the mean;
• mapping, by the one or more processors, the given entity for each sentence to a strongest emotion for the given entity, wherein the polarity of the strongest emotion for each given entity is the same as the polarity of the strongest sentiment for the given entity;
• determining, by the one or more processors, for each strongest emotion, if the strongest emotion is within the standard deviation from the mean;
• and
• based on determining that a strongest emotion of the strongest emotions is within the standard deviation from the mean, retaining the strongest emotion.

A computer program product for identifying key emotional content in a text, the computer program product comprising:

• one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media readable by at least one processing circuit to:
• obtain, by the one or more processors, the text from a computing resource in a domain;
• segment, by the one or more processors, the text into sentences;
• utilize, by the one or more processors, pre-defined entities for the domain, to identify entities in each sentence of the sentences comprising the text;
• classify, by the one or more processors, each entity of the identified entities with a sentiment and extracting the sentiments, wherein the classifying assigns a polarity to each sentiment of the sentiments;
• identify, by the one or more processors, for each sentence, a given entity with a strongest sentiment, wherein the identifying comprises quantitively determining a value for each of the sentiments and utilizing the values to identify the strongest sentiment;
• derive, by the one or more processors, for each sentence, for each entity, one or more emotions and classifying each emotion of the one or more emotions, wherein the classifying comprising:
• assign a polarity to each emotion;
• and
• associate a score with each emotion to reflect a strength of each emotion within the polarity;
• calculate, by the one or more processors, for the one or more emotions in each sentence of the text, based on the scores, a mean and a standard deviation from the mean;
• map, by the one or more processors, the given entity for each sentence to a strongest emotion for the given entity, wherein the polarity of the strongest emotion for each given entity is the same as the polarity of the strongest sentiment for the given entity;
• determine, by the one or more processors, for each strongest emotion, if the strongest emotion is within the standard deviation from the mean;
• and
• based on determining that a strongest emotion of the strongest emotions is within the standard deviation from the mean, retain the strongest emotion.