Automated Linguistic Personalization of Targeted Marketing Messages Mining User-Generated Text on Social Media

Abstract

Personalizing marketing messages for specific audience segments is vital for increasing user engagement with advertisements, but it becomes very resource-intensive when the marketer has to deal with multiple segments, products or campaigns. In this research, we take the first steps towards automating message personalization by algorithmically inserting adjectives and adverbs that have been found to evoke positive sentiment in specific audience segments, into basic versions of ad messages. First, we build language models representative of linguistic styles from user-generated textual content on social media for each segment. Next, we mine product-specific adjectives and adverbs from content associated with positive sentiment. Finally, we insert extracted words into the basic version using the language models to enrich the message for each target segment, after statistically checking in-context readability. Decreased cross-entropy values from the basic to the transformed messages show that we are able to approach the linguistic style of the target segments. Crowdsourced experiments verify that our personalized messages are almost indistinguishable from similar human compositions. Social network data processed for this research has been made publicly available for community use.