Sentiment analysis is applied across several domains: politics, where social-media text can be used to track public sentiment; public health, where emotional signals may support detecting depression and monitoring well-being; and brand management, stock-market analysis, and education.

Google

University of Michigan - Ann Arbor

Sentiment analysis is used to refer to the task of automatically determining the whether a piece of text is positive, negative, or neutral. It can also determine someone's attitude towards a particular target or topic.

Sentiment Analysis

1. Evaluation (good or bad)
2. Potency (strong or weak)
3. Activity (active or passive)

Dimensions of meaning of Sentiment Analysis of Text

Many of the machine learning approaches for automatic detection of sentiment are supervised at first. In other words, the systems first learn a model from a set of example instances labeled with the correct sentiment also known as the training set. Then the model is able to predict the sentiment of new instances. 

This paper also discusses the many angles that we can use sentiment analysis, for instances, we can detect the sentiment of the writer or reader, detect sentiment from different the words, sentence, paragraph level, and it can also detect the sentiment towards a target.

Process of Sentiment Analysis

In almost every language, the same word can often convey different sentiments in different contexts. For example, the word unpredictable is negative in the context of automobile steering, but positive in the context of a movie script. However, the majority of these words tend to convey the same sentiment in a large majority of the contexts they occur in. For instance, great and amazing are positive, and sad and depressed are negative in most cases when they are used. These majority associations are often referred to as prior associations, which is beneficial for sentiment analysis systems (where the system creates lists of term–sentiment associations by manual annotation).

Capturing Term-Sentiment Associations

Sentiment analysis systems evaluate text through several distinct detection tasks. These primarily include detecting subjectivity (distinguishing personal opinions from objective facts), measuring valence (determining the positive, negative, or neutral polarity of a text), and identifying specific underlying human emotions within texts such as sentences and tweets.

Core Detection Tasks in Sentiment Analysis

Application Areas of Sentiment Analysis

Sentiment analysis faces several challenges: creative or nonstandard language can be difficult to interpret; utterances may convey multiple emotions or describe emotional events without explicitly expressing the author's feelings; the same text can express different emotions across contexts or senses; and limited paralinguistic information or insufficient labeled training data can hinder machine-learning systems.

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