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% and Jaccard score of 0.61.

翻译：情感分析是一项旨在识别和提取文本情感层面的自然语言处理任务。然而，许多现有的情感分析方法主要对文本的整体情感极性进行分类，忽略了传达情感的具体短语。本文提出了一种基于问答框架的情感分析方法。该方法利用预训练的序列到序列模型——双向自回归Transformer（BART）的强大能力，从给定文本中提取出强化特定情感极性的短语。我们构建了一个自然语言问题来识别待提取的具体情感，进而引导BART关注文本中相关的情感线索。我们在BART内部使用一个分类器来预测答案片段在文本中的起始和结束位置，这有助于确定所提取情感短语的精确边界。与大多数情感分析研究相比，我们的方法具有若干优势，包括捕捉文本的完整上下文和语义，以及提取突出目标情感的精确词元片段。我们取得了87%的最终损失和0.61的Jaccard分数。