# Brand Intelligence Analytics

arXiv: https://arxiv.org/abs/2001.11479 · HTML: https://mlsanity.com/paper/2001.11479/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [As long as you talk about me: The importance of family firm brands and the contingent role of family-firm identity](https://mlsanity.com/paper/2110.13815/) (2021)
- [Forecasting consumer confidence through semantic network analysis of online news](https://mlsanity.com/paper/2105.04900/) (2021)
- [A new mapping of technological interdependence](https://mlsanity.com/paper/2308.00014/) (2023)
- [Corporate core values and social responsibility: What really matters to whom](https://mlsanity.com/paper/2106.01644/) (2021)
- [Tell me a story about yourself: The words of shopping experience and self-satisfaction](https://mlsanity.com/paper/2108.03016/) (2021)
- [Evaluating and improving social awareness of energy communities through semantic network analysis of online news](https://mlsanity.com/paper/2208.01892/) (2022)
- [Brand Network Booster: A new system for improving brand connectivity](https://mlsanity.com/paper/2309.16228/) (2023)
- [LCA and energy efficiency in buildings: mapping more than twenty years of research](https://mlsanity.com/paper/2409.00065/) (2024)
- [Measuring AI innovation with trademark data](https://mlsanity.com/paper/2607.18795/) (2026)

## Abstract (arXiv)

Leveraging the power of big data represents an opportunity for brand managers to reveal patterns and trends in consumer perceptions, while monitoring positive or negative associations of the brand with desired topics. This chapter describes the functionalities of the SBS Brand Intelligence App (SBS BI), which has been designed to assess brand importance and provides brand analytics through the analysis of (big) textual data. To better describe the SBS BI's functionalities, we present a case study focused on the 2020 US Democratic Presidential Primaries. We downloaded 50,000 online articles from the Event Registry database, which contains both mainstream and blog news collected from around the world. These online news articles were transformed into networks of co-occurring words and analyzed by combining methods and tools from social network analysis and text mining.