negative feelings) or happiness, love, or enthusiasm (i.e. Emotion detection aims at detecting emotions like, happiness, frustration, anger, sadness, and the like.Many emotion detection systems resort to lexicons (i.e.lists of words and the emotions they convey) or complex machine learning algorithms.
Sentiment analysis, just as many other NLP problems, can be modeled as a classification problem where two sub-problems must be resolved: In an opinion, the entity the text talks about can be an object, its components, its aspects, its attributes, or its features.
It could also be a product, a service, an individual, an organization, an event, or a topic.
In the following section, we’ll cover the most important ones.
Sometimes you may be also interested in being more precise about the level of polarity of the opinion, so instead of just talking about This is usually referred to as fine-grained sentiment analysis.
As an example, take a look at the opinion below: Usually, comparative opinions express similarities or differences between two or more entities using a comparative or superlative form of an adjective or adverb.
In the previous example, there's a positive opinion about camera A and, conversely, a negative opinion about camera B.
Some words that would typically express anger like ).
Usually, when analyzing the sentiment in subjects, for example products, you might be interested in not only whether people are talking with a positive, neutral, or negative polarity about the product, but also which particular aspects or features of the product people talk about.
It’s estimated that 80% of the world’s data is unstructured and not organized in a pre-defined manner.
Most of this comes from text data, like emails, support tickets, chats, social media, surveys, articles, and documents.