A related task to sentiment analysis is the subjectivity analysis with the goal of labeling an opinion as either subjective or objective.
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Neural models at the sentence level often operate on the constituent words/tokens in a way that encodes the inductive bias of processing the input in a similar fashion to how humans do.
While some tasks deal with identifying the presence of sentiment in the text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral.
We introduce a novel type of LM using a modified version of bidirectional LSTM (BLSTM) called contextual BLSTM (cBLSTM), where the probability of a word is estimated based on its full left and right contexts.
This paper surveys different ways used for building systems for subjective and sentiment analysis for languages other than English.