# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
###### Abstract  
In this paper, we present a novel self-supervised framework for Sentiment Cue Extraction (SCE) aimed at enhancing the interpretability of text sentiment analysis models. Our approach leverages self-supervised learning to identify and highlight key textual elements that significantly influence sentiment classification decisions. Central to our framework is the development of an innovative Mask Sequence Interpretation Score (MSIS), a bespoke metric designed to assess the relevance and coherence of identified sentiment cues within binary text classification tasks. By employing Monte Carlo Sampling techniques optimized for computational efficiency, our framework demonstrates exceptional effectiveness in processing large-scale text data across diverse datasets, including English and Chinese, thus proving its versatility and scalability. The effectiveness of our approach is validated through extensive experiments on several benchmark datasets, including SST-2, IMDb, Yelp, and ChnSentiCorp. The results indicate a substantial improvement in the interpretability of the sentiment analysis models without compromising their predictive accuracy. Furthermore, our method stands out for its global interpretability, offering an efficient solution for analyzing new data compared to traditional techniques focused on local explanations. sentiment cue extraction; self-supervised learning; interpretable machine learning +
Footnote †: journal: Medical Informatics  
0000-0001-9993-3888-2000/0000-0000-0000-00000-0000-00000-0  
Despite the remarkable advances and successes in sentiment analysis, a significant hurdle persists: the challenge of interpretability, which encompasses the difficulty of understanding and explaining how sentiment analysis models make their decisions, particularly in terms of identifying specific factors or textual elements that influence these decisions [4]. Traditional sentiment analysis models are often criticized for their \"black-box\" nature, which obscures the transparency of their decision-making processes [5]. This opacity generates concerns about accountability and dependability, especially in scenarios where precision and reliability are paramount. To mitigate these concerns, our research introduces a novel self-supervised framework focused on sentiment cue extraction. This approach involves the identification and extraction of crucial linguistic elements--such as specific words, phrases, or syntactic patterns, referred to as \"sentiment cues\" in this paper--that significantly influence sentiment determination. Our approach is instrumental in demystifying the decision-making process of sentiment analysis models, thus contributing to a deeper understanding and trust in these systems. For example, in finance, discerning the exact cues that drive sentiment predictions can be a game changer for market analysis [6; 7; 8]. Similarly, in healthcare, the analysis of sentiment cues in patient feedback, particularly from online sources, is essential to improve the quality of healthcare services. By evaluating positive and negative sentiments expressed in patient reviews, healthcare providers can identify strengths and areas for improvement in their services, such as facility cleanliness, staff behavior, and general patient care [9]. Our study introduces a groundbreaking framework based on self-supervised learning that incorporates sequence labeling techniques to significantly improve the interpretability of sentiment analysis models. Traditional approaches in sentiment cue extraction often involve labor-intensive and time-consuming data annotation processes. Existing interpretability methods for text classification models, while offering partial solutions, primarily depend on local interpretative methods. These local methods typically require individual training for each data instance, presenting significant challenges in efficiently handling new data. In contrast, our innovative approach uses the abundance of existing annotated sentiment classification data through self-supervised learning.