We examine different features and classifiers for the categorization of opinion words into actor and speaker view. To our knowledge, this is the first comprehensive work to address sentiment views on the word level taking into consideration opinion verbs, nouns and adjectives. We consider many high-level features requiring only few labeled training data. A detailed feature analysis produces linguistic insights into the nature of sentiment views. We also examine how far global constraints between different opinion words help to increase classification performance. Finally, we show that our (prior) word-level annotation correlates with contextual sentiment views.
Most computational approaches to metaphor detection try to leverage either conceptual metaphor mappings or selectional preferences. Both require extensive knowledge of the mappings/preferences in question, as well as sufficient data for all involved conceptual domains. Creating these resources is expensive and often limits the scope of these systems. We propose a statistical approach to metaphor detection that utilizes the rarity of novel metaphors, marking words that do not match a text's typical vocabulary as metaphor candidates. No knowledge of semantic concepts or the metaphor's source domain is required. We analyze the performance of this approach as a stand-alone classifier and as a feature in a machine learning model, reporting improvements in F 1 measure over a random baseline of 58% and 68%, respectively. We also observe that, as a feature, it appears to be particularly useful when data is sparse, while its effect diminishes as the amount of training data increases.
We offer a critical review of the current state of opinion role extraction involving opinion verbs. We argue that neither the currently available lexical resources nor the manually annotated text corpora are sufficient to appropriately study this task. We introduce a new corpus focusing on opinion roles of opinion verbs from the Subjectivity Lexicon and show potential benefits of this corpus. We also demonstrate that state-of-the-art classifiers perform rather poorly on this new dataset compared to the standard dataset for the task showing that there still remains significant research to be done.
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