Social media is an obvious source of insights about the public's interactions with one's brands and products. No marketing department in a modern organization fails to have one or more social media accounts in order to publicize their marketing efforts but also to collect feedback in the form of likes, mentions, retweets, and so on. Some social media services such as Twitter provide APIs for keyword searches to identify relevant comments by users all around the world. However, these keyword searches are limited to text – it's not possible to find tweets that, say, include a photo of a particular brand.

However, using DL, we can make our own image filter, and thereby detect a previously untapped source of feedback from social media. We will focus on Twitter and use somewhat generic keyword searches to find tweets with photos. Each photo will then be sent through a custom classifier to identify if any logos of interest are found in the photos. If found, these photos, and the tweet content and user information, is saved to a file for later processing and trend analysis.

The same technique would be applicable to other social media platforms that include images, such as Reddit. Interestingly, the largest photo sharing service, Instagram, is deprecating their public API at the end of 2018 due to privacy concerns. This means that it will no longer be possible to obtain publicly shared photos on Instagram. Instead, API access will be limited to retrieving information about a business Instagram account, such as mentions, likes, and so on.

We will look at two techniques for recognizing logos in photos:

We then build a small Java tool that monitors Twitter for photos and sends them to YOLO for detection and recognition. If one of a small set of logos is found, we save relevant information about the tweet and the detected logos to a CSV file.