WordNet-Affect is an extension of WordNet Domains, including a subset of synsets suitable to represent affective concepts correlated with affective words. Similarly to our method for domain labels, we assigned to a number of WordNet synsets one or more affective labels (a-labels). In particular, the affective concepts representing emotional state are individuated by synsets marked with the a-label emotion. There are also other a-labels for those concepts representing moods, situations eliciting emotions, or emotional responses.

The resource was extended with a set of additional a-labels (called emotional categories), hierarchically organized, in order to specialize synsets with a-label emotion. The hierarchical structure of new a-labels was modeled on the WordNet hyperonym relation. In a second stage, we introduced some modifications, in order to distinguish synsets according to emotional valence. We defined four addictional a-labels: positive, negative, ambiguous, and neutrald.

The first one corresponds to positive emotions, defined as emotional states characterized by the presence of positive edonic signals (or pleasure). It includes synsets such as joy#1 or enthusiasm#1. Similarly the negative a-label identifies negative emotions characterized by negative edonic signals (or pain), for example anger#1 or sadness#1. Synsets representing affective states whose valence depends on semantic context (e.g. surprise#1) were marked with the tag ambiguous. Finally, synsets referring to mental states that are generally considered affective but are not characterized by valence, were marked with the tag neutral.

An other important property for affective lexicon concerning mainly adjectival interpretation is the stative/causative dimension. An emotional adjective is said causative if it refers to some emotion that is caused by the entity represented by the modified noun (e.g. amusing movie). In a similar way, an emotional adjective is said stative if it refers to the emotion owned or felt by the subject denoted by the modified noun (e.g. cheerful/happy boy).

A-Labels and corresponding example synsets

A-Labels Examples
emotion noun anger#1, verb fear#1
mood noun animosisy#1, adjective amiable#1
trait noun aggressiveness#1, adjective competitive#1
cognitive state noun confusion#2, adjective dazed#2
physical state noun illness#1, adjective all in#1
hedonic signal noun hurt#3, noun suffering#4
emotion-eliciting situation noun awkwardness#3, adjective out of danger#1
emotional response noun cold sweat#1, verb tremble#2
behaviour noun offense#1, adjective inhibited#1
attitude noun intolerance#1, noun defensive#1
sensation noun coldness#1, verb feel#3

Application for Affect Sensing

All words can potentially convey affective meaning. Each of them, even those more apparently neutral, can evoke pleasant or painful experiences. While some words have emotional meaning with respect to the individual story, for many others the affective power is part of the collective imagination (e.g. words mum, ghost, war etc.).

Therefore, it is interesting to individuate a way to measure the affective meaning of a generic term. To this aim, we studied the use of words in textual productions, and in particular their co-occurrences with the words in which the affective meaning is explicit. We have to distinguish between words directly referring to emotional states (e.g. fear, cheerful) and those having only an indirect reference that depends on the context (e.g. words that indicate possible emotional causes as monster or emotional responses as cry). We call the former direct affective words and the latter indirect affective words.

We organized direct affective words and synsed in WordNet-Affect. Then, we developed a selection function (named Affective-Weight) based on a semantic similarity mechanism automatically acquired in an unsupervised way from a large corpus of texts (100 millions of words), in order to individuate the indirect affective lexicon.

Applied to a concept (e.g. a WordNet synset) and an emotional category, this function returns a value representing the semantic affinity with that emotion. In this way it is possible to assign a value to the concept with respect to each emotional category, and eventually select the emotion with the highest value. Applied to a set of concepts that are semantically similar, this function selects subsets characterized by some given affective constraints (e.g. referring to a particular emotional category or valence).

We are able to focus selectively on positive, negative, ambiguous or neutral types of emotions. For example, given difficulty as input term, the system suggests as related emotions (a-labels): identification, negative-concern, ambiguous-expectatioin, apathy. Moreover, given an input word (e.g. university) and the indication of an emotional valence (e.g. positive), the system suggests a set of related words through some positive emotional category (e.g. professor, scholarship, achievement) found through the emotions enthusiasm, sympathy, devotion, encouragement.

These fine-grained kinds of affective lexicon selection can open up new possibilities in many applications that exploit verbal communication of emotions.


C. Strapparava and A. Valitutti. Wordnet-affect: an affective extension of wordnet. In Proceedings of the 4th International Conference on Language Resources and Evaluation, Lisbon, 2004.

A. Valitutti, C. Strapparava, and O. Stock. Developing affective lexical resources. Psychnology: 2 (1), 2004.

C. Strapparava, A. Valitutti, and O. Stock. The affective weight of lexicon. In Proceedings of the Fifth International Conference on Language Resources and Evaluation, 1-83, 2006.


If you want to exchange ideas, report errors, or propose possible applications and development lines, please write to:
Carlo Strapparava (strappafbk.eu) and Alessandro Valitutti (alvalitufbk.eu).