A Contextual Classification Strategy for Polarity Analysis of Direct Quotations from Financial News

Quotations from financial leaders can have significant influence upon the immediate prospects of economic actors. Indiscreet or candid comments from senior business leaders have had detrimental effects upon their organizations. Established polarity classification techniques perform poorly when classifying quotations because they display a number of complex linguistic features and lack of training data. The proposed strategy segments the quotations by inferred “opinion maker” role and then applies individual polarity classification strategies to each group of the segmented quotations. This strategy demonstrates a clear advantage over applying classical classification techniques to the whole corpus of quotations. While modelling contextual information with Random Forests based on a vector of unigrams plus the “opinion maker role” reaches a maximum F-measure of 52.85%, understanding the “bias” of the quotation maker previously based on its lexical usage allows 86.23% F-measure for “unbiased” quotations and 71.10% F-measure for “biased” quotations with the Naive Bayes classifier.