The dictionary-based and TL approaches were evaluated on two manually annotated datasets, working with two annotators: in the first dataset (~21,000 sentences), the annotation task was that of identifying evaluative language (vs. descriptive language); in the second dataset (~13,500 sentences), the task focused on the distinction between positive and negative sentiment. These two classification tasks form the basis for a large-scale analysis of the LOBO corpus, which segments reviews into evaluative and descriptive passages, to describe differences in evaluation practices across genres (e.g., romance, science fiction) and ratings (1-5 stars).