Mining Comparative Opinions in Portuguese: A Lexicon-based Approach
DOI:
https://doi.org/10.5753/jbcs.2024.2830Keywords:
Opinion Mining, Sentiment Analysis, Comparative Opinions, Preference DetectionAbstract
The constant expansion of e-commerce, recently boosted due to the coronavirus pandemic, has led to a massive increase in online shopping, made by increasingly demanding customers, who seek comments and reviews on the Web to assist in decision-making regarding the purchase of products. In these reviews, part of the opinions found are comparisons, which contrast aspects expressing a preference for an object over others. However, this information is neglected by traditional sentiment analysis techniques and it is not applicable for comparisons, since they do not directly express positive or negative sentiment. In this context, despite efforts in the English language, almost no studies have been done to develop appropriate solutions that allow the analysis of comparisons in the Portuguese language. This work presented one of the first studies on comparative opinion in Portuguese. Four main contributions are (1) A hierarchical approach for detecting comparative opinions, which consists of an initial binary step, which subdivides the regular opinions from the comparatives, to further categorize the comparatives into the five opinion groups: (1) Non-Comparative; (2) Non-Equal Gradable; (3) Equative, (4) Superlative; and (5) Non-Gradable. The results are promising, reaching 87% of Macro-F1 and 0.94 of AUC (Compute Area Under the Curve) for the binary step, and 61% of Macro-F1 in multiple classes; (2) An lexicon algorithm to detect the entity expressed as preferred in comparative sentences, reaching 94% of Macro-F1 for Superlative; (3) Two new datasets with approximately 5,000 comparative and non-comparative sentences in Portuguese; and (4) a lexicon with words and expressions frequently used to make comparisons in the Portuguese language.
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Copyright (c) 2024 Daniel Kansaon, Michele A. Brandão, Júlio C. S. Reis, Fabrício Benevenuto
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