9. A method of applying role based association to entities in textual documents, the method comprising: obtaining a plurality of textual documents describing a plurality of entities; identifying a relationship between entities and verbs, at least on a sentence level, based on a semantic analysis of the verbs; determining at least one of: a topic, and a sentiment associated to the verbs identified in said textual documents or to sentences associated with said verbs; and using the identified relationship between entities and verbs and a respective determined topic or sentiment associated with the verbs, to determine for each of the plurality of entities: a sentiment level associated thereto, and a level of activity at the at least one topic associated thereto; applying a probabilistic model of the topics presented in the textual documents while using the identified relationship between entities and verbs and a respective determined topic or sentiment associated with the verbs to determine for each of the plurality of entities: a sentiment level associated thereto, and a level of activity at the at least one topic associated thereto, taking into account a probability of each of the topic in a respective sentence, wherein the probabilistic model at the sentence level is based on: P k , s ′ = θ k , d * ∑ w ⁢ ⁢ in ⁢ ⁢ s ⁢ ⁢ φ k , w wherein P′ k,s represents an approximated probability of each topic k occurring in sentence s, wherein θ k,d denotes probability of topic k occurring in document d and wherein φ k,w denotes a probability of word w occurring in topic k, wherein an Active Actor in Topic (AAIT) score is calculated for each entity e in topic k (AAIT k,e ) as a sum of the approximated proportions of topic k in all sentences in the document d using, where the entity is tagged as active so that: AAIT k , e = ∑ s ⁢ ⁢ in ⁢ ⁢ d ⁢ ⁢ P k , s ′ * A e , s .
9. A method of applying role based association to entities in textual documents, the method comprising: obtaining a plurality of textual documents describing a plurality of entities; identifying a relationship between entities and verbs, at least on a sentence level, based on a semantic analysis of the verbs; determining at least one of: a topic, and a sentiment associated to the verbs identified in said textual documents or to sentences associated with said verbs; and using the identified relationship between entities and verbs and a respective determined topic or sentiment associated with the verbs, to determine for each of the plurality of entities: a sentiment level associated thereto, and a level of activity at the at least one topic associated thereto; applying a probabilistic model of the topics presented in the textual documents while using the identified relationship between entities and verbs and a respective determined topic or sentiment associated with the verbs to determine for each of the plurality of entities: a sentiment level associated thereto, and a level of activity at the at least one topic associated thereto, taking into account a probability of each of the topic in a respective sentence, wherein the probabilistic model at the sentence level is based on: P k , s ′ = θ k , d * ∑ w ⁢ ⁢ in ⁢ ⁢ s ⁢ ⁢ φ k , w wherein P′ k,s represents an approximated probability of each topic k occurring in sentence s, wherein θ k,d denotes probability of topic k occurring in document d and wherein φ k,w denotes a probability of word w occurring in topic k, wherein an Active Actor in Topic (AAIT) score is calculated for each entity e in topic k (AAIT k,e ) as a sum of the approximated proportions of topic k in all sentences in the document d using, where the entity is tagged as active so that: AAIT k , e = ∑ s ⁢ ⁢ in ⁢ ⁢ d ⁢ ⁢ P k , s ′ * A e , s . 10. The method according to claim 9 , wherein the determining of a topic associated to the verbs is carried out directly at the verb level and so the AAIT k,e score is calculated as a sum of all verbs associated with topic k where entity e is tagged as active.