1. A computer implemented method for analysing natural language contained in electronic text to determine a sentiment between two entities discussed in the natural language, comprising the following steps: receiving, via an input module, the electronic text containing the natural language at a processing circuitry; using an input/output subsystem of the processing circuitry to move the received natural language to a data storage; analysing the natural language in the data storage to determine a syntactic representation which shows syntactic constituents of the analysed natural language and to determine a sentiment score of each constituent, wherein each constituent is a sentiment (sub)context, and wherein determining a sentiment score of each constituent comprises: determining a polarity distribution of each (sub)context, and calculating an entity sentiment score for each entity from the cumulative polarity distributions D 1 . . . D n across all (sub)contexts z 1 . . . z n , the entity sentiment score for polarity p of entity e in sentence s with n (sub)contexts being obtained through the equation: e_scr ⁢ ( p , e ) = ∑ i = 1 n ⁢ fD i wherein z is Constituent as sentiment (sub)context p is Polarity pε{POS, NTR, NEG} assigned to z, by sentiment grammar D i ⁢ ⁢ is ⁢ ⁢ Polarity ⁢ ⁢ distribution ⁢ ⁢ of ⁢ ⁢ z j := { f if ⁢ ⁢ p ⁢ ⁢ is ⁢ ⁢ POS f if ⁢ ⁢ p ⁢ ⁢ is ⁢ ⁢ NEG 0.5 · f if ⁢ ⁢ p ⁢ ⁢ is ⁢ ⁢ NTR s is Sentence d is Dijkstra's shortest path distance between sentence root and head of z i β is Coverage coefficient: length(z i )/length(s) f is Weight ƒ or z i : d + β length ⁡ ( s ) ; determining which constituents link the two entities; calculating an overall sentiment score for the sentiment between the two entities by processing the sentiment score of each constituent of the constituents determined to link the two entities; and outputting, by a computer processor, the overall sentiment score for the sentiment between the two entities discussed by the natural language contained in the electronic text.
1. A computer implemented method for analysing natural language contained in electronic text to determine a sentiment between two entities discussed in the natural language, comprising the following steps: receiving, via an input module, the electronic text containing the natural language at a processing circuitry; using an input/output subsystem of the processing circuitry to move the received natural language to a data storage; analysing the natural language in the data storage to determine a syntactic representation which shows syntactic constituents of the analysed natural language and to determine a sentiment score of each constituent, wherein each constituent is a sentiment (sub)context, and wherein determining a sentiment score of each constituent comprises: determining a polarity distribution of each (sub)context, and calculating an entity sentiment score for each entity from the cumulative polarity distributions D 1 . . . D n across all (sub)contexts z 1 . . . z n , the entity sentiment score for polarity p of entity e in sentence s with n (sub)contexts being obtained through the equation: e_scr ⁢ ( p , e ) = ∑ i = 1 n ⁢ fD i wherein z is Constituent as sentiment (sub)context p is Polarity pε{POS, NTR, NEG} assigned to z, by sentiment grammar D i ⁢ ⁢ is ⁢ ⁢ Polarity ⁢ ⁢ distribution ⁢ ⁢ of ⁢ ⁢ z j := { f if ⁢ ⁢ p ⁢ ⁢ is ⁢ ⁢ POS f if ⁢ ⁢ p ⁢ ⁢ is ⁢ ⁢ NEG 0.5 · f if ⁢ ⁢ p ⁢ ⁢ is ⁢ ⁢ NTR s is Sentence d is Dijkstra's shortest path distance between sentence root and head of z i β is Coverage coefficient: length(z i )/length(s) f is Weight ƒ or z i : d + β length ⁡ ( s ) ; determining which constituents link the two entities; calculating an overall sentiment score for the sentiment between the two entities by processing the sentiment score of each constituent of the constituents determined to link the two entities; and outputting, by a computer processor, the overall sentiment score for the sentiment between the two entities discussed by the natural language contained in the electronic text. 5. A method according to claim 1 , wherein processing the sentiment score of each constituent of the constituents determined to link the two entities comprises using a windowed method to include a plurality of entities.