Subjectivity detection classifies a sentence or tweet as subjective, expressing a personal opinion, or objective, presenting factual information. Detection techniques may use patterns of word usage, particular kinds of adjectives, and emotional terms. The resulting classification can support tasks such as question answering and paraphrasing.

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University of Michigan - Ann Arbor

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

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.

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