info_exchange_zscore module

features.info_exchange_zscore.get_info_exchange_wordcount(df, first_person, message_col)

This functinon computes the total word count in a message minus first person singular pronouns.

Note that the function assumes that lexical features and basic features have already been run, and have generated a raw count of first-person pronouns (stored in “first_person_raw”) and a total number of words (stored in “num_words”).

This value then serves as an input into a Z-score (calculated using a function in Utilities); the idea is to compute the extent to which a team exchanges more or fewer “content words” outside of first-person pronouns, which is a rough measure of “information exchange.”

Because the original paper was not specific about what the z-scores were computed with respect to, we compute one version with respect to each chat in a single conversation, and one version with respect to all chats in the entire dataset of all conversations.

Source: Tausczik and Pennebaker (2013); https://www.cs.cmu.edu/~ylataus/files/TausczikPennebaker2013.pdf

Parameters:

• df (pd.DataFrame) – This is a pandas dataframe of the chat level features.

• first_person (list) – A list of first person words. This comes from get_first_person_words() under the Utilities.

• message_col (str) – This is a string with the name of the column containing the message / text.

Returns:

A column containing the difference in total words and first-person singular pronouns.

Return type:

pd.Series