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.

University of Michigan - Ann Arbor

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

Detecting Subjectivity in Sentences and Tweets

Throughout the development of sentiment analysis systems, there have been tremendous interest in accurately determining valence in sentences and tweets, as indicated by various surveys. In these natural language systems, textual instances are often represented as vectors in a feature space, and is converted into binary, which can later be trained and observe whether the word is a positive or negative term.
More recently, there have been significant improvements classification accuracy that have been obtained through low-dimensional continuous representations of instances and words. This method relies on the lotion that these vectors have only a few hundred dimension, and continuous refers to the real-valued nature of the dimensions, and that is the dimension does not have just 0 or 1 values, but can have any real number values, which can make the result more accurate.

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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