The English writer, Virginia Woolf, once said: “Every secret of a writer’s soul, every experience of his life, every quality of his mind is written large in his works.” Sentiment analysis has a similar purpose to help understand an author’s written words about a subject by identifying and extracting subjective information from the text data.
According to Brand 24, sentiment analysis is: “…the process of analyzing online pieces of writing to determine the emotional tone they carry, whether they’re positive, negative, or neutral. In simple words, sentiment analysis helps to find the author’s attitude towards a topic.”
Sentiment analysis is similar to other Emotion Artificial Intelligence (Emotion AI) tools like emotion recognition that measures facial expressions to determine emotions and share similar goals and challenges.
How does it work?
Sentiment analysis algorithms use a combination of natural language processing (NLP) techniques and machine learning algorithms. NLP techniques extract and analyze text data, while machine learning algorithms train the model to recognize and classify different emotions.
Researchers might use one of four different types of analysis methods (TechTarget):
- Fine-grained sentiment analysis: similar to a 5-star scale from positive to negative
- Emotion detection: breaks down feedback even further into specific emotions such as happiness or sadness
- Intent-based analysis: looks at action-oriented opportunities such as having a hotel representative reach out to a customer who left a negative comment about their stay
- Aspect-based analysis: searches for the specific characteristic behind a positive or negative comment, such as a malfunction in a particular part of a product
Sentiment analysis functions using two methods of machine learning:
- Supervised machine learning: trains on a large dataset of labeled text data. The algorithm learns to detect different patterns and features associated with different emotions. The trained algorithm is then used to categorize the sentiment of new text data.
- Unsupervised machine learning: does not train on labeled data but learns to identify and categorize emotions on its own. This works best with a large amount of unlabeled data so that the algorithm can learn and adapt to new data with explicit training.
How does sentiment analysis support Emotion AI?
Sentiment analysis and Emotion AI combined provide a powerful tool for researchers looking to better understand their audiences. Much like with any emotion-based tool, emotions are not always the same person to person, which presents a big challenge in trying to identify them.
A further challenge of Sentiment Analysis may be that relying on human language – which varies according to colloquialisms (or slang), the variety of expressions and so on – can make it difficult for algorithms to decipher them which, of course, they do not yet detect all the characteristics of natural language. However, this is where the combination of Emotion AI and sentiment analysis comes into play.
Emotion AI relies on years of research that shows the universality of emotions in facial expressions. We cover this more in detail in our glossary of Emotion AI. But in short, sentiment analysis provides an extra layer of insight to understanding the emotions behind an audience and helps confirm findings following an analysis with emotion AI software (like EmPower).
Why does sentiment analysis matter?
Several use cases exist, from social media monitoring to customer feedback analysis and language translation. In each case, researchers move one step closer to understanding their audience better and thus make informed decisions with positive results.
Sentiment analysis matters because it enhances our ability to understand and interpret the emotional content of text data. And as previously mentioned, when combined with other tools such as Emotion AI, can become quite powerful.