Learning sentiment composition from sentiment lexicons

A system comprising:

• at least one hardware processor;
• and
• a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to:
• generate, from a text corpus, a lexicon of unigrams and bigrams comprising an embedding for each of said unigrams and bigrams;
• train a machine learning classifier on a training set comprising a subset of said lexicon, wherein each of said unigrams and bigrams in said subset has a sentiment label;
• apply said machine learning classifier to said lexicon, to (i) predict a sentiment of each of said unigrams and bigrams, and (ii) update said lexicon with the predicted sentiments;
• perform statistical analysis on said updated lexicon, to extract one or more sentiment composition lexicons, wherein:
• each of said one or more sentiment composition lexicons is associated with a sentiment composition class,
• with respect to each said sentiment composition class, said statistical analysis comprises determining a statistical ratio between (i) all bigrams in said updated lexicon meeting said conditions of said sentiment composition class, and (ii) all bigrams in said updated lexicon meeting said conditions of said sentiment composition class, whose sentiment is equal to said predicted sentiment associated with said sentiment composition class;
• and
• predict a sentiment bigram comprising a first unigram from a said sentiment composition lexicon based, at least in part, on meeting one or more conditions of said associated sentiment composition class, wherein said conditions comprise at least one of (i) an ordering of said first unigram and a second unigram within said bigram, (ii) a sentiment of said first unigram, and (iii) a sentiment of said second unigram.