Extracting sentiment as a function of discourse structure and topicality

An approach to extracting sentiment from texts that makes use of con- textual information and an enhancement of the previous word-based methods in the treatment of intensi…ers and negation, and the addition of other parts of speech beyond adjectives.

Abstract

We present an approach to extracting sentiment from texts that makes use of con- textual information. Using two dierent approaches, we extract the most relevant sentences of a text, and calculate semantic orientation weighing those more heavily. The …rst approach makes use of discourse structure via Rhetorical Structure Theory, and extracts nuclei as the relevant parts; the second approach uses a topic classi…er built using support vector machines, which extracts topic sentences from texts. The use of weights on relevant sentences shows an improvement over word-based methods that consider the entire text equally. In the paper, we also describe an enhancement of our previous word-based methods in the treatment of intensi…ers and negation, and the addition of other parts of speech beyond adjectives.