# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 2 Related Works
### Self-Supervised Methods for Information Extraction  
Self-supervised learning in NLP has emerged as a fundamental approach to information extraction, harnessing the potential of unlabeled data to train predictive models. This paradigm involves creating learning tasks in which models predict certain parts of the input using other parts [21; 22; 23].  
By utilizing large volumes of unlabeled data, self-supervised learning allows models to learn rich representations. These representations are beneficial for diverse downstream NLP tasks, especially valuable in contexts where labeled data are scarce or expensive to acquire [16; 21].  
Among the popular methodologies in self-supervised learning, Masked Language Modeling (MLM) stands out. MLM is a key technique in self-supervised learning, notably used by BERT. It involves hiding some words in a sentence and training the model to predict these hidden words using the surrounding context. This process aids in understanding the context and relationships between words [16].  
Permutation-based language modeling, as introduced by XLNet, is another significant methodology. It extends the concept of MLM to predict a token based on all permutations of tokens in a sentence. This approach offers a more comprehensive context understanding [22].  
Additionally, models like BART [23] and Text-to-Text Transfer Transformer (T5) [24] utilize a corrupted text generation task for pre-training. In this approach, models learn to reconstruct the original text from a corrupted version, thereby enhancing their understanding of language structure and coherence [23].  
In the evolving landscape of self-supervised learning models, the Generative Pre-trained Transformer (GPT) series by OpenAI marks a pivotal juncture [17; 25; 26]. Unlike BERT, renowned for its bidirectional approach to language comprehension, GPT models excel at text generation by predicting the subsequent word in a sequence. Consequently, while BERT shines in nuanced language understanding tasks, GPT excels in producing coherent and contextually apt text.  
Continuing this trajectory, ChatGPT ([[https://chat.openai.com](https://chat.openai.com)]([https://chat.openai.com](https://chat.openai.com)) (accessed on 19 March 2024)), a notable addition to the GPT lineage, heralds further breakthroughs. Specifically, ChatGPT exemplifies the prowess of large-scale language models across an array of uses, from crafting human-like narratives to conducting nuanced sentiment analyses. Its adaptability for fine-tuning targeted tasks significantly expands its utility and effectiveness in addressing diverse NLP challenges. Parallel to ChatGPT's emergence, a myriad of other large language models like Gemini ([[https://gemini.google.com/](https://gemini.google.com/)]([https://gemini.google.com/](https://gemini.google.com/)) (accessed on 19 March 2024)) and ERNIE Bot ([[https://yiyan.baidu.com/](https://yiyan.baidu.com/)]([https://yiyan.baidu.com/](https://yiyan.baidu.com/)) (accessed on 19 March 2024)) have surfaced, enriching the field with their distinct contributions.  
However, these advancements are not without challenges. ChatGPT's closed-source nature hinders research transparency and restricts community-driven enhancements. Moreover, the substantial computational resources required to operate or fine-tune such models often necessitate reliance on cloud-based APIs provided by the developers. This reliance raises concerns regarding cost-effectiveness, latency issues, and data privacy implications [27; 28].  
Self-supervised learning has achieved remarkable success in tasks such as named entity recognition, relation extraction, and event extraction. By pretraining on extensive text corpora, these models capture nuanced language patterns, significantly increasing their task performance [29; 30].