Evaluation of an Algorithm for Aspect-Based Opinion Mining Using a Lexicon-Based Approach

Abstract

In this paper, we present a study of aspect-based opinion mining using a lexicon-based approach. We use a phrase-based opinion lexicon for the German language to investigate, how good strong positive and strong negative expressions of opinions, concerning products and services in the insurance domain, can be detected. We perform experiments on hand-tagged statements expressing opinions retrieved from the Ciao platform. The initial corpus contained about 14,000 sentences from 1,600 reviews. For both, positive and negative statements, more than 100 sentences were tagged. We show, that the algorithm can reach an accuracy of 62.2% for positive, but only 14.8% for negative utterances of opinions. We examine the cases, in which the opinion could not correctly be detected or in which the linking between the opinion statement and the aspect fails. Especially, the large gap in accuracy between positive and negative utterances is analysed.

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