A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment
  Analysis
Jingyi Zhou Jie Zhou Jiabao Zhao Siyin Wang Haijun Shan Gui Tao Qi Zhang Xuanjing Huang
Few-shot text classification has attracted great interest in both academia
and industry due to the lack of labeled data in many fields. Different from
general text classification (e.g., topic classification), few-shot sentiment
classification is more challenging because the semantic distances among the
classes are more subtle. For instance, the semantic distances between the
sentiment labels in a positive or negative polarity (e.g., ``love" and ``joy",
``remorse" and ``sadness") are close, while the distances are large for the
sentiment labels in two opposite polarities (e.g., ``love" and ``sadness"). To
address this problem, we propose a Soft Contrastive learning-based Prompt
(\texttt{SCP}) model for few-shot sentiment analysis. First, we design a
sentiment-aware chain of thought prompt module to guide the model to predict
the sentiment from coarse grain to fine grain via a series of intermediate
reasoning steps. Then, we propose a soft contrastive learning algorithm to take
the correlation of the labels into account. A series of experiments on several
sentiment analysis datasets show the great advantages of \texttt{SCP} by
comparing it with SOTA baselines (e.g., ChatGPT).