One of the most distinguished features of humanity, is being able to read someone else’s mood. People estimate other peoples mood and emotional state based on their verbal and non-verbal communication. Humans learn this skill from a very early age on and are able to distinguish the signs even for different people. For example, we all know when our best friend is feeling down, even when they’re trying to hide it for others. Maybe by being louder than usual, or they might be more quiet. But you know that they are troubled. Being able to distinguish this for different people is due to experience and how close you are to the other person. The years of friendship make it easier to read her mood and know when something is wrong.
Sentiment analysis
But what if I tell you a computer can also do this? And they don’t have to ‘be your friend’ for years and years. Using various techniques such as Natural Language Processing (NLP), the computer is able to recognise words and sentences as emotions. Words like ‘stress’ and ‘feeling alone’ are registered as negative, whilst ‘glad’ and ‘exited’ as marked as positive. This is called a sentiment analysis. Some sentiment analysis even link certain combinations of words to feelings, such as depressed, sad, cheerful or hopeful.
Possibilities
But what are the possible use cases of the sentiment analysis? And what are the challenges? Sentiment analysis is for example already being used in healthcare. Based on the unstructured notes a psychologist takes during sessions with their client, the sentiment analysis can track the clients mood over time. In this case, it includes possible diagnoses and ‘trigger words’ such as suicidal. This allows the psychologist to have one overview of the client’s emotional and mental state over time.
But what if this is taken one step further. What if this is done based on social media. Think about it: Your stories, chat messages, posts, web usage, emoticon usage, everything is documented and examined on your emotional state.
Challenges
One of the most important questions that arises from this, is regarding to privacy. Who is allowed when to measure your mental state based on your social media usage and your google searches? This is very sensitive (health)data, not stated by a doctor, but estimated by a computer. Another challenge is interpreting the words correctly, for example: ‘good’ is a positive word but ‘really not good’ is a negative combination of words. The complexity of languages can make it hard for a computer to interpret it correctly. However, Artificial Intelligence (AI) is often used for this so that the estimations become more accurate with each analysis.
Sources
Abualigah, L., Alfar, H., Shehab, M., & Hussein, A. (2019). Sentiment Analysis in Healthcare: A brief Review. In M. Abd Elaziz, M. Al-quaness, A. Ewees, & A. Dahou, Recent Advances in NLP: The Case of Arabic Language (pp. 129-141). Switzerland: Springer, Cham. doi:10.1007/978-3-030-34614-0_7
Denecke, K., & Deng, Y. (2015, March 25). Sentiment analysis in medical settings: New opportunities and challenges. Artificial Intelligence in Medicine, pp. 17-27. doi:10.1016/j.artmed.2015.03.006
