def sentiment(
        self,
        text: str,
        criteria: str = None,
        examples: Optional[List[str]] = None,
        min_score_val: int = 0,
        max_score_val: int = 3,
        temperature: float = 0.0,
    ) -> float:
        """
        Uses chat completion model. A function that completes a template to
        check the sentiment of some text.

        Example:
            ```python
            feedback = Feedback(provider.sentiment).on_output()
            ```

        Args:
            text (str): The text to evaluate sentiment of.
            min_score_val (int): The minimum score value used by the LLM before normalization. Defaults to 0.
            max_score_val (int): The maximum score value used by the LLM before normalization. Defaults to 3.

        Returns:
            A value between 0 and 1. 0 being "negative sentiment" and 1
                being "positive sentiment".
        """

        output_space = self._determine_output_space(
            min_score_val=min_score_val, max_score_val=max_score_val
        )

        system_prompt = feedback_v2.Sentiment.generate_system_prompt(
            min_score=min_score_val,
            max_score=max_score_val,
            criteria=criteria,
            examples=examples,
            output_space=output_space,
        )

        user_prompt = feedback_prompts.SENTIMENT_USER + text
        return self.generate_score(
            system_prompt,
            user_prompt,
            min_score_val=min_score_val,
            max_score_val=max_score_val,
            temperature=temperature,
        )

        Uses chat completion model. A function that completes a template to
        check the sentiment of some text.

        Example:
            ```python
            feedback = Feedback(provider.sentiment).on_output()
            ```

        Args:
            text (str): The text to evaluate sentiment of.
            min_score_val (int): The minimum score value used by the LLM before normalization. Defaults to 0.
            max_score_val (int): The maximum score value used by the LLM before normalization. Defaults to 3.

        Returns:
            A value between 0 and 1. 0 being "negative sentiment" and 1
                being "positive sentiment".
        
src/feedback/trulens/feedback/llm_provider.py