1. A method, in a data processing system, for applying a genetic algorithm to semantic sentiment analysis, the method comprising: providing, by the data processing system, a sentiment analysis model to a sentiment analysis algorithm; training, by the data processing system, the sentiment analysis model using a genetic algorithm based on a training corpus of documents with corresponding desired sentiment analysis values for a given domain to form a trained sentiment analysis model, wherein training the sentiment analysis model using the genetic algorithm comprises for each given training document in the training corpus: performing the sentiment analysis algorithm on the given training document to form a sentiment analysis result; making modifications to the sentiment analysis model to form a plurality of individual models: identifying a best fitness individual model from the plurality of individual models using the genetic algorithm, wherein the best fitness individual model minimizes a distance from desired sentiment analysis values corresponding to the given training document, wherein fitness of each individual model is based on an absolute value of a distance from a generated sentiment value to a desired sentiment value weighted by an amount of change from an initial value in the sentiment analysis model; and storing the best fitness individual model as the trained sentiment analysis model; performing, by the data processing system, the sentiment analysis algorithm on an input document using the trained sentiment analysis model to form a domain-specific sentiment analysis result; outputting, by the data processing system, the domain-specific sentiment analysis result; providing the domain-specific sentiment analysis result to a question answering system; and performing analysis of an input question or a candidate answer in the question answering system using the domain-specific sentiment analysis result.