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This work introduces a new method to consider subjectivity and generalcontext dependency in text analysis and uses as example the detection ofemotions conveyed in text. The proposed method takes into account subjectivityusing a computational version of the Framework Theory by Marvin Minsky (1974)leveraging on the Word2Vec approach to text vectorization by Mikolov et al.(2013), used to generate distributed representation of words based on thecontext where they appear. Our approach is based on three components: 1. aframework/'room' representing the point of view; 2. a benchmark representingthe criteria for the analysis - in this case the emotion classification, from astudy of human emotions by Robert Plutchik (1980); and 3. the document to beanalyzed. By using similarity measure between words, we are able to extract therelative relevance of the elements in the benchmark - intensities of emotionsin our case study - for the document to be analyzed. Our method provides ameasure that take into account the point of view of the entity reading thedocument. This method could be applied to all the cases where evaluatingsubjectivity is relevant to understand the relative value or meaning of a text.Subjectivity can be not limited to human reactions, but it could be used toprovide a text with an interpretation related to a given domain ("room"). Toevaluate our method, we used a test case in the political domain.

A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text