Introduction of Quantifiers and Negations Sample Clauses

Introduction of Quantifiers and Negations. In the preliminary implementation of Sentiment Analysis, we have started with dictionary-based polarity detection, with no regard for the syntactic context of the polar words. In this phase, we take into account this context by marking quantifiers and negations listed in an in-house dictionary. We take into account quantifiers and negations in the aggregate polarity calculation in the same way as Xxxxxxxxx-Xxxxxxx et al. (2013) (see also Deliverable D4.1 p46). Quantifiers are only used to distinguish sentiment words which act as quantifiers, such as pretty in pretty mad. The word pretty is both a positive polar word and a quantifier. We want its polarity to be positive in case it occurs in isolation, but less than one so that the sum with a following negative polar word (such as mad) be negative. On the contrary, the presence of negations may reverse the aggregate polarity of the segment. and Table 10 show the effect of taking into account quantifiers and negations in the aggregate polarity calculation, respectively on the detection of opinionated tweets and on the polarity classification (positive or negative) of these opinionated tweets. We observe that negations and quantifiers have little impact on the detection of opinionated opinionated tweets (Table 9: Detection of opinionated tweets without taking into account (“Without”) and taking into account (“With”) negations and quantifiers ). Taking negations into account allows an improvement of the classification of negative tweets, but at the cost of a worsening of the classification of positive ones (Table 10: Precision, Recall and F-measure for polarity classification without taking into account (“Without”) and taking into account (“With”) negations and quantifiers; POS and NEG refer to the positive and negative classes). Opinion Finder SenticNet LIWC Without With Without With Without With Precision (%) 73.3 73.4 75.3 75.3 75.2 75.2 Recall (%) 68.5 68.3 37.0 36.8 33.7 33.7 F-measure (%) 70.9 70.8 49.6 49.4 46.5 46.5 Table 9: Detection of opinionated tweets without taking into account (“Without”) and taking into account (“With”) negations and quantifiers‌ Opinion Finder SenticNet LIWC Without With Without With Without With Precision (%) POS 76.6 74.7 91.3 88.1 57.0 57.0 NEG 50.2 52.6 26.0 31.9 65.5 65.5 Recall (%) POS 86.9 87.2 85.7 86.3 86.4 86.4 NEG 33.3 32.5 38.1 35.5 28.4 28.4 F-measure (%) POS 81.5 80.4 88.4 87.2 68.7 68.6 NEG 40.0 40.2 30.9 33.6 39.6 39.6 Table 10: Precision, Recall and F-measure f...
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