The role of Image Analytics in Social Media Monitoring
Up to now, apart from having the ability to access the relevant posts, text analytics and Natural Language Processing (NLP) have been the main disciplines required by social media monitoring tools. That is not the case anymore.
Tweets with an image get retweeted 150% more than those without one; they also get liked 89% more. According to Twitter, 77% of all tweets about soft drinks do not have a textual reference to a soft drink brand or anything related to the product category. What?????
So if you are Coca Cola or Pepsi Cola, using social media monitoring tools to crawl the web in order to harvest all the relevant posts about your brands, you will miss out on a great deal of them if you are searching based on keywords only. Even if you just get a hold of the 23% that do include one of your keywords, you will still have no idea what the images included in those are about.
What if the author of a post wrote: “Music and beer…great combination!” and posted the image below?
A social media monitoring tool would never tag this post as one about Heineken. Only a social listening tool specifically developed for marketing insights purposes such as listening247 can offer this capability.
Given all the stats shared above – about the use of images in social media – it is unimaginable how any serious brand owner will continue to only monitor text in social media. Clearly, image analytics needs to become part of the insight management process of any company or organisation using social media listening.
How is it done?
Easy! You need a Data Scientist who can get you a convolutional neural network with over 15 layers – Deep Learning is anything over 4 layers – you find or create a training data set of at least 100,000 images to start with, you get your hands on a VERY powerful computer with multiple Graphic Processors and lots of RAM, or get access to a Big Data infrastructure in the Cloud, and you train a model for a few days. Piece of cake!
What Image Elements can be analysed and why do we analyse them?
There are a number of things that can be analysed in images that can be useful for market research:
- Logo detection
- Text extraction
- Object recognition
- Facial recognition to detect emotions
- Theme detection and captioning
Let’s talk about the use of each one of them separately:
- Logo detection is an obvious one; we need to be able to find the images that include the logos of the brands in the competitive set so that we can extract information and analyse it.
- Some Twitteratis game the system by using text in an image in addition to the 140 characters that Twitter allows for a Tweet.
- This is useful when looking for a specific item or product where the logo is not visible
- Detecting consumer emotions in images where brand logos appear can be very useful in understanding how consumers feel about a brand
- By knowing what an image is about we can cluster it under its respective discussion driver “bucket”. An image can now be part of a topic taxonomy for holistic semantic analysis.
The obvious conclusion is that text analytics alone does not cut it for social media monitoring anymore; image analytics as described above is necessary, in order to understand what consumers think and feel when they post online. 3rd Generation Social Listening is here. Click here to experience the DigitalMR “Magic Captioner” and its A.I. magic yourself.
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