Social Media Marketing is useless! …I think
Every day hundreds of Social Media Managers sit in front of analysis tools and puzzle over which campaign was a success.
Do 3000 Likes justify the budget or are 50 comments a success?
Social networks map relations between humans, whether personal profile, influencer or marketeer in an enterprise.
Relationships cannot be expressed over simple counting metrics. Interaction with social media content always has a reference to the topic and is processed emotionally. Then an action takes place, such as like, comment or buy.
For the first time, Artificial Intelligence offers social media managers the opportunity to look inside the relationships beyond likes, comments and engagement rate.
Artificial Intelligence in Social Media Marketing
With DDA, social media managers gain quick and deep insights into the performance of campaigns, influencers and contributions:
- Comprehensible: Social networks communicate in speech. No more abstraction of likes, DDA extracts topics and expressed emotions.
- At all levels: Whether brand, service or product: DDA unerringly identifies entities that are being talked about.
- Emotions: No product or company exists in a vacuum. DDA goes further than positive or negative, each statement is broken down into eight basic emotions and can therefore be analysed further.
- Hitting the right tone: Topics and emotions can be combined with performance values. No more gut instincts as to whether the tonality corresponds to the users.
How can DDA help me?
How is my brand generally perceived?
Why all the rage?
You have a specific question?
Show me all the topics that where perceived joyful since November 2018.
Here you go:
…Social Media Managers who really want to know which contents work.
…Agencies who want to analyze their influencer portfolio in depth.
…Companies who want to understand how brand, products and services really reach their customers.
Artificial intelligence, really?
Artificial intelligence is a household word. The term often evokes associations with popular films such as Terminator, Avengers or Matrix.
The popular cultural references to artificial intelligence are what is described as general artificial intelligence. A program that is capable of human-like thought processes, or as John McCarthy defined it back in 1956: AI involves machines that can perform tasks that are characteristic of human intelligence.
In return there is Narrow AI, a program that can do a thing from the spectrum of human intelligence very well, e.g. speech comprehension.
In the environment of artificial intelligence the term Machine Learning is often used synonymously. Machine Learning is the process that leads to artificial intelligence, the training of algorithms on large data sets from which the system can learn to improve performance.
Deep Learning is a special case of Machine Learning. It is based on neural networks which are modelled in their structure on the human brain. In neural networks there are different neurons which learn from a specific feature and are linked to other neurons. Depth is created by layering many layers of neurons on top of each other.
Deep Data Analytics uses deep neural networks to analyze texts.
So are you doing social media sentiment analysis? Boring
Sentiment analysis has long been used as a term in the computer sciences, linguistics and social sciences. So what is so great about DDA?
Sentiment analysis is usually understood as the determination of the polarity of an expression or a word, so the term is positive or negative. Put simply, there are lists of words and each word is assigned a positive, negative or neutral sentiment.
A simple example:
The term unfortunately nice is broken down in this bag of words approach into unfortunately = negative, nice = positive, in the result a neutral expression.
In contrast, neural networks can learn idioms, youth language, sarcasm and irony and reliably recognize them. In the concrete case our system recognizes the expression “unfortunately nice” as colloquially positive.
With DDA we go one step further:
Besides positive or negative, our systems break down every utterance into eight basic emotions in order to get a more detailed picture. We follow the US-American psychologist Paul Eckmann, who has dealt with emotionality in language.