deletions | additions       diff --git a/Humour_Classification_Scoring_cite_Mihalcea2006__.md b/Humour_Classification_Scoring_cite_Mihalcea2006__.md  index 0097c4f..927c346 100644  --- a/Humour_Classification_Scoring_cite_Mihalcea2006__.md  +++ b/Humour_Classification_Scoring_cite_Mihalcea2006__.md 
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- describing a model for recognizing these phenomena in social media, such as “tweets"  - five data sets retrieved from Twitter taking advantage of user-generated tags, such as “#humor" and “#irony"  - irony detection [44,45,10,35], satire detection [9], and sarcasm detection [43,18]  -features:  - ambiguity, concerning with three layers: structural, morphosyntactic and semantic - the perplexity of a set of funny texts against that of non-funny ones.  - we think that the number of POS tags that any word in context can have, is a hint at the underlying mechanism of humor to produce its effect  - we defined a measure to statistically estimate the range of semantic dispersion profiled by a text in order to determine how ambiguous this text is  -  polarity, concerning with words that denote either positive or negative semantic orientation - unexpectedness, concerning with contextual imbalances among conveying  the opposite meaning by profiling positive qualities over negative ones  - unexpectedness  - the lesser  semantic meanings of relatedness,  the words greater contextual imbalance (funny/ironic texts)  - emotional scenarios, concerning with psychological contexts regarding natural language concepts. - categories quantify emotional words in terms of scores obtained from human ratings regarding natural language  - activation (degree of response, either passive or active, that humans have under an emotional state), imagery (how difficult it is to form a mental picture of a given word), and pleasantness (degree of pleasure produced by the words)