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The vast area of subjectivity in Natural Language Processing (NLP) poses achallenge to the solutions typically used in generalized tasks. As explorationin the scope of generalized NLP is much more advanced, it implies thetremendous gap that is still to be addressed amongst all feasible tasks wherean opinion, taste, or feelings are inherent, thus creating a need for asolution, where a data fusion could take place. We have chosen the task offunniness, as it heavily relies on the sense of humor, which is fundamentallysubjective. Our experiments across five personalized and four generalizeddatasets involving several personalized deep neural architectures have shownthat the task of humor detection greatly benefits from the inclusion ofpersonalized data in the training process. We tested five scenarios of trainingdata fusion that focused on either generalized (majority voting) orpersonalized approaches to humor detection. The best results were obtained forthe setup, in which all available personalized datasets were joined to trainthe personalized reasoning model. It boosted the prediction performance by upto approximately 35% of the macro F1 score. Such a significant gain wasobserved for all five personalized test sets. At the same time, the impact ofthe model's architecture was much less than the personalization itself. Itseems that concatenating personalized datasets, even with the cost ofnormalizing the range of annotations across all datasets, if combined with thepersonalized models, results in an enormous increase in the performance ofhumor detection.

From Generalized Laughter to Personalized Chuckles: Unleashing the Power of Data Fusion in Subjective Humor Detection