.. _positivity_z_score: Positivity Z-Score =================== High-Level Intuition ********************* This feature measures how positive an utterance is relative to other messages. This positivity is gauged by comparing each message against all messages in a dataset or within the same conversation. Citation ******** `Tausczik & Pennebaker (2013) `_ Implementation ************** A positivity score is first assigned using `RoBERTa; Liu et al., (2019) `_. We then compute two types of z-scores based on this positivity score: 1. **Positivity among all messages (`positivity_zscore_chats`)**: - This z-score measures how a message's positivity compares to all other messages in the entire dataset. 2. **Positivity within the same conversation (`positivity_zscore_conversations`)**: - This z-score measures how a message's positivity compares to other messages within the same conversation (grouping by the unique conversational identifier). Interpreting the Feature ************************* The positivity z-scores can be both negative and positive, with no fixed bounds. - **Negative Score**: Indicates that the utterance is less positive compared to other messages (either in the entire dataset or within the same conversation, depending on the reference point of the z-score). - **Zero Score**: Indicates that the utterance has a typical (average) level of positivity compared to other messages. - **Positive Score**: Indicates that the utterance is more positive compared to other messages. Related Features **************** This feature is part of a broader category of sentiment analysis features. Other related features include: - :ref:`positivity_bert` - Negative_Emotion and Positive_Emotion (attributes of :ref:`politeness_receptiveness_markers`) - :ref:`textblob_polarity` - :ref:`liwc` (e.g., positive_affect_lexical_per_100) These features collectively help analyze and interpret the sentiment conveyed in messages.