Fine-grained sentiment analysis (FSA) aims to extract and summarize user opinions from vast opinionated text. Recent studies demonstrate that large language models (LLMs) possess exceptional sentiment understanding capabilities. However, directly deploying LLMs for FSA applications incurs high inference costs. Therefore, this paper investigates the distillation of fine-grained sentiment understanding from LLMs into small language models (SLMs). We prompt LLMs to examine and interpret the sentiments of given reviews and then utilize the generated content to pretrain SLMs. Additionally, we develop a comprehensive FSA benchmark to evaluate both SLMs and LLMs. Extensive experiments on this benchmark reveal that: (1) distillation significantly enhances the performance of SLMs in FSA tasks, achieving a 6.00\% improvement in $F_1$-score, and the distilled model can outperform Llama-2-7b with only 220M parameters; (2) distillation equips SLMs with excellent zero-shot sentiment classification capabilities, enabling them to match or even exceed their teacher models. These results suggest that distillation from LLMs is a highly promising direction for FSA. We will release our code, data, and pretrained model weights at https://github.com/HITSZ-HLT/FSA-Distillation.

Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment analysis task that aims to extract the corresponding opinion words of a given opinion target from the sentence. Recently, deep learning approaches have made remarkable progress on this task. Nevertheless, the TOWE task still suffers from the scarcity of training data due to the expensive data annotation process. Limited labeled data increase the risk of distribution shift between test data and training data. In this paper, we propose exploiting massive unlabeled data to reduce the risk by increasing the exposure of the model to varying distribution shifts. Specifically, we propose a novel Multi-Grained Consistency Regularization (MGCR) method to make use of unlabeled data and design two filters specifically for TOWE to filter noisy data at different granularity. Extensive experimental results on four TOWE benchmark datasets indicate the superiority of MGCR compared with current state-of-the-art methods. The in-depth analysis also demonstrates the effectiveness of the different-granularity filters. Our codes are available at https://github.com/TOWESSL/TOWESSL.

Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data comes from a different domain than the test data. On the other hand, some rule-based unsupervised methods have shown to be robust when applied to different domains. In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for different domains. When this hidden representation is used for opinion target extraction, we find that it can outperform a number of strong baselines with a large margin.

In this paper, we present a simplified shallow semantic parsing approach to extracting opinion targets. This is done by formulating opinion target extraction (OTE) as a shallow semantic parsing problem with the opinion expression as the predicate and the corresponding targets as its arguments. In principle, our parsing approach to OTE differs from the state-of-the-art sequence labeling one in two aspects. First, we model OTE from parse tree level, where abundant structured syntactic information is available for use, instead of word sequence level, where only lexical information is available. Second, we focus on determining whether a constituent, rather than a word, is an opinion target or not, via a simplified shallow semantic parsing framework. Evaluation on two datasets shows that structured syntactic information plays a critical role in capturing the domination relationship between an opinion expression and its targets. It also shows that our parsing approach much outperforms the state-of-the-art sequence labeling one.