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.

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

There are many challenges in sentiment analysis. 
1. The use of creative and non-standard language in text can be difficult to interpret by the automatic natural language systems.
2. Utterances sometimes can convey more than one emotion or refer to emotional events without explicitly or implicitly to express the emotions/feelings of the author of the text.
3. Text that were used in different contexts and different senses can sometimes express different feelings/emotions.
4. There might also be a lack of paralinguistic information or lack of large amounts of labeled data that will be difficult for many machine learning algorithms to understand.

Challenges in 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

Process of Sentiment Analysis

Currently, sentiment analysis is used in many areas of our lives. For example, sentiment analysis is used in politics for tracking the public sentiment especially in social media, and it is also used in public health to detect emotions that might signals depression and tracking well being. Besides these, it is also used in brand management, stock market, and education.

Sentiment Analysis areas

Currently, sentiment analysis systems have been applied to many different kinds of texts including novels, news paper headlines, emails, etc. These systems can detect the subjectivity, valence, and emotions in sentences and tweets.

Detecting Subjectivity, Valence, and Emotions in Sentences and Tweets

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).

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