Extracting Emotion Phrases from Tweets using BART
CoRR(2024)
摘要
Sentiment analysis is a natural language processing task that aims to
identify and extract the emotional aspects of a text. However, many existing
sentiment analysis methods primarily classify the overall polarity of a text,
overlooking the specific phrases that convey sentiment. In this paper, we
applied an approach to sentiment analysis based on a question-answering
framework. Our approach leverages the power of Bidirectional Autoregressive
Transformer (BART), a pre-trained sequence-to-sequence model, to extract a
phrase from a given text that amplifies a given sentiment polarity. We create a
natural language question that identifies the specific emotion to extract and
then guide BART to pay attention to the relevant emotional cues in the text. We
use a classifier within BART to predict the start and end positions of the
answer span within the text, which helps to identify the precise boundaries of
the extracted emotion phrase. Our approach offers several advantages over most
sentiment analysis studies, including capturing the complete context and
meaning of the text and extracting precise token spans that highlight the
intended sentiment. We achieved an end loss of 87
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