Abstract—In Sentiment Analysis (SA), a vague assignment of a text to a set of n-ary discrete classes is insufficient. A great deal of research is concentrated on the automated assignment of strength to both terms and the finer-grained term senses, but these strength values rely purely on statistical means, and there is no semantic mechanism involved, leading to potentially biased results. As a solution, this works proposes a model that utilizes only the semantic information manually encoded within the human-defined glosses of term senses, a semantic network, and a set of predefined degree adverbs, in order to quantify their ‘Natural’ Sentiment Strength (NSS) values. The ‘natural’ sentiment strength of a term sense here refers to the strength value derived in a ‘semantically natural’ manner, i.e. the NSS is assigned based on the agreed-upon meanings that humans have naturally assigned to words; and not ‘artificially statistical’, i.e. based a simple metric of probabilistic computation. Intrinsic evaluation against a manually-annotated gold standard benchmark demonstrates that the model outperforms related sense-level lexicon generation models against this same benchmark, and that it is in agreement with human intuition.
 

Index Terms—sentiment analysis, opinion mining, sentiment lexicon, sentiment strength, sentiment lexicon generation

Cite: Mohammad Darwich, Shahrul Azman Mohd Noah, Nazlia Omar, Nurul Aida Osman, and Ibrahim Said Ahmad, "Quantifying the Natural Sentiment Strength of Polar Term Senses Using Semantic Gloss Information and Degree Adverbs," Journal of Advances in Information Technology, Vol. 11, No. 3, pp. 109-118, August 2020. doi: 10.12720/jait.11.3.109-118

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