ABSTRACT
This chapter outlines sentiment analysis approaches that combine text analysis methods similar to define relevant metrics that would help meet the goal of improving our understanding of consumer content in digital social media. Building on sentiment analysis approaches developed by computer scientists and linguists, researchers analyze a tweet and assign it a numerical score indicative of its valence. The central premise of our argument here is that the persuasive, informative, as well as complementary effects of advertising, which have been documented and researched extensively in the past for traditional forms of advertising, serve as a strong theoretical basis for defining meaningful metrics for improving our understanding of content in digital social media. The procedure proposed above allows us to assess the content within each digital social interaction in terms of its ability to generate persuasive, informative, and complementary effects akin to the theoretical effects identified in the previous literature.
