What are the differences between emotional and sentiment recognition?

The techniques of machine-learning data exploration and prediction empowering Artificial Intelligence include Natural Language Processing which partially specialises in sentiment and emotion recognition.

While many people regard a sentiment and an emotion as synonyms, the scientists working with AI are not using these terms interchangeably. Emotions and sentiments are different data; hence, they require different analytical techniques. The aim of this article is to compare the two types of analyses from both the theoretical point of view and the view of practical appliance.

Understanding emotions and sentiments

Obviously, sentiments and emotions are forms of a mental state of a person, however, their meanings are not the same. Emotions are complex descriptions of feelings such as happiness or anger, although the complexity of a sentiment goes even further. This is a presentation of a certain opinion or a thought produced by a scope of emotions about a particular event.

While emotions are a raw form of a human’s reaction, sentiments can be viewed as structures based on emotions and thoughts connected to a certain situation.

The basic ideas of sentiment and emotional analyses

Considering the meaning of sentiments explained above, we might assume sentiment analysis should be more advanced than the analysis of emotions.

Yet, for NLP the opposite is true. In fact, many scientists see emotional analysis as a higher form of evolution of sentiment analysis because of the intricacy of the machine learning techniques used for emotional recognition.

Despite the intensity of a thoughtful process required for generating sentiments, a computer observes humans’ sentiments through the prism of polarity. Algorithms can see sentiments as either negative or positive with an intermediate state of neutrality. Some analytical approaches can also distinguish intensity of sentiments and label them as very negative or very positive.

Consider such phrases as “The product is amazing” and “The product met my expectations”.  At this point, we ignore the hidden emotional scope and see no difference between the two phrases. The sentiment of both of them is positive.

Emotional analysis can be challenging for humans themselves who quite frequently fail to perceive right not only the emotions of other individuals but also of their own. The machine-learning analysis of emotions applies extensive vocabulary for categorisation aiming to distinguish exact types of emotions. For instance, for negative emotions, there will be a difference between anger, rage, madness, frustration and sadness.

The technology of sentiment and emotional analyses

One of the most common approaches to the detection of emotions and sentiment of speech includes an intermediate process of speech-to-text transcription. This means the pre-processing step transforms audio materials into textual data for further linguistic analysis.

It is also possible to focus specifically on the quality of sound waves generated by humans’ speech applying the Theory of Sound and Acoustics. Here, the physical parameters of the sound will determine either a sentiment or an emotion depending on the scope of research. Such an approach differs from the machine-learning techniques typical for NLP since it omits the actual linguistic meaning of the speech.

Despite the differences between sentiments and emotions from the point of view of modern machine-learning algorithms, both approaches can be applied for their analyses. Yet, in the case of the technology based on linguistics, emotional analysis is more advanced as it combines several methods.


For AI, emotional analysis is a sophisticated method giving more insights than sentiment analysis which offers oversimplified results. Deeper understanding of responses offered by emotional analysis is promising for industrial appliance, albeit, companies should consider the possible costs of this technique and remember about the importance of building natural human connections with their clients.