Q. How does the opinion-mining engine in sentic computing classify affective information?

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Q. What is the primary purpose of the preprocessing module in the opinion-mining engine?

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Q. Why is handling negation important in opinion- and sentiment-related analysis?

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Q. What is the role of constructions in the parsing process of the sentic parser?

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Q. How does the sentic parser efficiently choose the best construction for each span of text?

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Q. What does the Sentic parser provide for each retrieved concept?

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Q. What is the purpose of the IsaCore and AffectiveSpace modules in analyzing the small bag of concepts (SBoC)?

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Q. How is coarse sense disambiguation performed when multiple senses of a concept are detected?

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Q. What is the role of seeds in the IsaCore module?

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Q. How is the classification measure of concepts in the SBoC determined?

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Q. What does the AffectiveSpace module do with the concepts from each SBoC?

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Q. How are concepts categorized in the AffectiveSpace module?

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Q. What is the affective information used for in the sentic extraction process?

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Q. How does the engine ensure fast real-time opinion mining?

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Q. What is the approximate processing time for the extraction of semantics and sentics when using SQL databases?

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Q. What is the primary challenge posed by the growth of user-generated content on the Web 2.0?

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Q. What is the multidisciplinary approach recommended for analyzing big social data?

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Q. What is the ultimate goal of a concept-level analysis of online social data?

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Q. What do next-generation opinion mining systems require to better understand natural language opinions?

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Q. What does the multidisciplinary approach aim to bridge more efficiently?

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