The problem you are encountering has little to do with whether the language model is state-of-the-art, but rather the language dialect(s) used to train the model versus those dialects used at inference time.
Most likely you are using a model trained on standard English, such as the text that appears in Wikipedia, whereas your second query uses the slang term ‘gooood’ which is likely outside the vocabulary used during training.    I suspect that you would encounter similar issues with ‘goooooood’, ‘soooo gooood’, vulgarities, acronyms (e.g.: lol, WTF) or words whose slang definition may differ from standard English (e.g.: bad, fly).
Dialect can be an important issue when dealing with casual speech, tweets, medical literature, clinical records, patents or other domains that use specialized language variants.
Possible approaches:

Select a pre-trained model trained on the language dialect of interest.  For example, cardiffnlp/twitter-roberta-base-sentiment is a variant of RoBERTa trained on tweets.  The huggingface model library also includes models trained on Reddit text.
Fine-tune an existing pre-trained model through supplementary training using examples of the language dialect of interest.  Huggingface includes datasets related to Reddit and tweets that may be of use for this task.
Using an existing base language model trained on the dialect of interest (e.g.: Reddit), and a classification head and train using a standard sentiment analysis training dataset