## 3 Proposed Methodology
### Dataset Structure  
The SST-2 dataset is a popular benchmark dataset widely used in NLP tasks, particularly for sentiment analysis. This dataset appears to represent a syntactic parse tree, or constituency tree, commonly used in NLP to represent the grammatical structure of sentences. Each number corresponds to a particular syntactic category or level of nesting within the sentence, and the brackets indicate hierarchical relationships between phrases or words. For example, extracting from the first part of the validation set, it describes a sentence like \"It's a nice movie with nice performances by [PERSON] and [PERSON]\". The nested numbers probably represent phrase structures, where 2 may represent a noun or verb phrase, 3 could be an adjective phrase, and 4 could represent deeper nested structures within the sentence. This type of data structure is often seen in treebanks, such as the Penn Treebank, where each sentence is parsed into a tree structure that reflects its syntactic composition. It is useful for training models for syntactic parsing or for tasks that rely on understanding sentence structure, such as semantic analysis or sentiment classification. Each phrase and word are tagged with a sentiment score ranging from 1 to 5, as follows: 1: very negative; 2: negative; 3: neutral; 4: positive; and 5: very positive. For example, we extract from \"(4 (3 (2 A) (4 ignant)) (3 (2,) (4 artfully) (3 (2 (3 (3 crafted) (2 (2 on) (2 mortality))))))\" in which the sentiment labels are placed at the beginning of the nodes, with the entire sentence receiving an overall sentiment of 4 (positive), 2 (negative), and 2 (neutral). Overall, this sentence shows a positive sentiment because the word \"poignant\" and the overall combination of phrases tilt the sentence towards a positive score of 4. The dataset is derived from movie reviews, with sentences extracted from movie reviews labeled as either positive or negative based on the sentiment expressed.  
SST-2 consists of a comprehensive set of sentences extracted from movie reviews, reflecting the diverse expressions of sentiment within cinematic discourse. Each sentence is carefully paired with a sentiment label that indicates whether the sentiment conveyed is positive or negative. This meticulous annotation process ensures the reliability of the dataset and facilitates accurate evaluation of sentiment analysis models. By including a wide variety of sentences and sentiment labels, SST-2 serves as an invaluable resource for exploring the complexities of sentiment classification in natural language and advancing the capabilities of NLP systems. SST-2, constructed from an extensive pool of movie reviews, presents a rich tapestry of sentences that encapsulate diverse moviegoing experiences and opinions. These sentences encapsulate a spectrum of emotions, ranging from glowing endorsements to scathing critiques. Each sentence is meticulously paired with a sentiment label, carefully crafted to reflect the nuanced polarity of the sentiment expressed, whether it be an enthusiastic endorsement or a critical appraisal. This meticulous annotation process ensures the reliability of the dataset and facilitates accurate evaluation of sentiment analysis models. By including a wide variety of sentences and sentiment labels, SST-2 serves as an invaluable resource for exploring the complexities of sentiment classification in natural language and advancing the capabilities of NLP systems. Figure 1 is a sample of some of the sentences extracted from the dataset.  
SST-2 uses a binary sentiment classification approach that categorizes sentences as positive or negative, simplifying sentiment analysis into unambiguous categories. This framework enhances usability and aligns with real-world applications by emphasizing the essential linguistic cues and semantic features underlying sentiment expression.