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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Large pre-trained language models (LMs) have demonstrated remarkable ability as few-shot learners.
Ranked #1 on Paraphrase Identification on Quora Question Pairs
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.
This paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis in Twitter.
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.
Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining.
This paper surveys different ways used for building systems for subjective and sentiment analysis for languages other than English.