Linguistic Personalization

Rishiraj Saha Roy

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.

Project Publications

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

Roy, R., Padmakumar, A., Jeganathan, G., Kumaraguru, P. (Apr. 14, 2015)
Best Paper Award 16th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing '15)

Generating topic-oriented summaries using neural attention

Krishna, K., Srinivasan, B. (Jun. 6, 2018)
16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

Vocabulary-tailored summary generation

Krishna, K., Murherkar, A., Sharma, S., Srinivasan, B. (Aug. 20, 2018)
27th International Conference on Computational Linguistics (COLING)