Detecting Subjectivity in Sentences and Tweets is one of the earliest problems tackled in sentiment analysis. For example sentences can be classified as subjective or objective. This has applications in  areas such as question answering and paraphrasing. Currently, there are a number of techniques that have been proposed to detect subjectivity using patterns of word usage, identifying certain kinds of adjectives, detecting emotional terms, etc.

Detecting Subjectivity in Sentences and Tweets

Detecting valence in sentences and tweets involves determining the polarity—positive, negative, or neutral—of the text. Early sentiment analysis systems often represented textual instances as binary vectors in a feature space to train classifiers. More recently, low-dimensional continuous representations, where vector dimensions take on real-number values rather than just 0 or 1, have significantly improved the classification accuracy of valence detection models.

Detecting Valence in Sentences and Tweets

Detecting emotions can be considered to be the most difficult when it comes to sentiment analysis because all the emotions theories associated with detecting emotions. For example, a research paper by Paul Ekman and others believe that some emotions are more basic than others and emotions are associated with universal facial expressions and heart rate. However, not everyone agrees with this theory, which makes detecting emotions difficult. This result in different ongoing projects for detecting emotions, and all of these projects use different sets of data and different theories.

Automatically detecting and Analyzing Emotions

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.

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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

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

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

There are several key challenges in sentiment analysis:

1. Creative and non-standard language is difficult for automatic natural language systems to interpret.
2. Utterances can convey multiple emotions or refer to emotional events without explicitly expressing the author's feelings.
3. The same text used in different contexts and senses can express different emotions.
4. A lack of paralinguistic information and large amounts of labeled training data can hinder machine learning algorithms.

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