Social networks are already part of our day to day. Users of these networks use them to comment and share content on topics that arouse their interest: hobbies, culture, politics, economy… and brands and their products or services.
Analyzing emotions in social networks such as Facebook, Twitter, and Instagram has become an essential element for companies when measuring the emotion of the millions of users who write and share content about brands, give likes, or retweet information about them—these or about the competition.
What is the analysis of emotions in social networks?
The analysis of emotions consists of automatically extracting through the analysis of data the anger, love, surprise, shame, euphoria, etc., that the users’ comments on the social networks give off.
Emotion analysis is part of Natural Language Processing (NLP) and the task called Sentiment Analysis or sentiment analysis and consists of going one step beyond the analysis of the opinion (positive, negative, or neutral) that we are analyzing.
When we analyze the emotion, we reach a higher level of detail:
- The user has a negative opinion of the brand and also shows an emotion of sadness
- The user has a positive opinion and also shows euphoria, joy, love, etc.
What is emotion analysis used for?
Currently, the analysis of emotions, like the analysis of sentiment, is closely linked to social networks, since brands are interested in knowing what users think of their brand, seeing their reputation, detecting crises or successes, making decisions of business, etc.; and all this, quickly and efficiently.
Emotion analysis is also used to enrich data from:
- Call Centers: detect the anger or satisfaction of customers who call with questions.
- Work environment surveys: detect the level of satisfaction or dissatisfaction of employees in their work.
- Product evaluation: detect the degree of satisfaction of buyers of your brand’s products to predict future purchases of the same or similar products.
Other applications of emotion analysis
It should be noted that emotions and opinions are key to brands or organizations since, many times, the opinions of others influence us in making our own decisions.
Emotional analysis for online stores: e-commerce website, whether for books, hotels, or travel, where the added value is the collection of user opinion. Let’s see some examples:
Online store
“Poor quality of the fabric; I will not buy again.”
Restaurant
“I did not like it too much. The salty menu was regular, and the cake they told me was better. On top of that, they are very picky about the hours that the table is occupied, and you have to “warn” if you are going to eat.”
Hotel
“Very central, modern, and well-priced hotel. The rooms are beautiful and functional, and the breakfast is spectacular.”
The analysis of emotions on social networking platforms such as Twitter or Facebook, where what happens on them, on many occasions, is news. These platforms are becoming another channel of communication with users. An example:
Blood donation campaigns throughout Spain at Christmas. Social networks moved users to donate blood in December 2017.
How are emotions extracted automatically?
Emotions are extracted automatically through a sentiment analysis engine. To do this, sentiment analysis uses Natural Language Processing to extract information from user mentions, opinions, and emotions. Basically, there are two natural language processing models:
- linguistic models, based on grammar and linguistic resources
- probabilistic models, based on annotated data and machine learning
Within linguistic models, there are different degrees of depth:
- Lexical analysis. The analysis engines reach a more superficial lexical or word level (the sentence with the word excellent will be positive, and the one with the word horrible will be negative).
- More complex analyses. The analysis engines not only remain in a lexical point of view but also advance to a morphological, syntactic, and context level (it can recognize positive structures with negative words, recognize the context, etc. Thus, it would recognize as positive the statement “I loved the movie about the mummy’s curse” or the double polarity of the following: “I’m not enthusiastic about the hotel, but I recognize that the location is unbeatable”), reaching greater precision, as is the case with the Lynguo tool.
In the analysis of emotions, he usually distinguishes between 8 to 20 different emotions, depending on the system used. Systems with 20 emotions are capable of distinguishing emotions such as love, euphoria, pleasure, joy, anger, disappointment, hate, sadness, and surprise…
Let’s see some examples:
Love
And then you are a father and discover the meaning of “unconditional love” With Stella in London #happyfathersday
Happiness
The joy of bringing water to people who need it
Admiration
It’s the news of the year!!! Very excited and proud, there is no one better than her ( Admiration and Joy )
Surprise
People are amazed to see Leticia Sabater singing the Toma Pepinazo. I am more amazed seeing Pedro Sánchez singing La Internacional.
Take it. What a surprise my students gave me last year!
Disappointment
I’m sorry, but to insert games like FIFA or golf crap, you have the real sports, I don’t know.
Anger
We are sick of hearing so many atrocities from you!!! What a shame!!!!!
The Treasury technicians remind us how harmful and unfair it is to suppress the tax.
Can you imagine everything you could do if you detected your followers’ and customers’ joy or anger on social networks?
Also Read : Getting Social Start-Ups To Communicate
