Politeness/Receptiveness Markers

High-Level Intuition

A collection of conversational markers that indicates the use of politeness / receptiveness.

Citation

Yeomans et al., (2020)

SECR Module (For computing features from Yeomans et al., 2020)

Implementation Basics

We follow a very similar framework to the SECR Module to compute a 39 politeness features for each chat in a conversation. The chats are first preprocessed in the following ways:

  1. Convert all words to lowercase

  2. Remove/expand contractions (i.e don’t to do not; can’t to cannot; let’s to let us)

  3. Ensure all characters are legal traditional A-Z alphabet letters by using corresponding RegExs

We then calculate the general categories of features in different ways, following similar structure as the SECR module.

  1. count_matches and Adverb_Limiter: calculates features using a standard bag-of-words approach, detecting the number of keywords from a pre-specified list stored in keywords.py.

  2. get_dep_pairs/get_dep_pairs_noneg: use Spacy to get dependency pairs for relevant words, using token.dep_ to differentiate with negation.

  3. Question: Question-related features are computed by counting the number of question words in a chat.

  4. word_start: detect certain conjunctions/affirmation words using pre-specified dictionary

The corresponding counts are then returned concatenated to the original dataframe.

Implementation Notes/Caveats

NA

Interpreting the Feature

The SECR module contains the following 39 features.

  • Impersonal_Pronoun

  • First_Person_Single

  • Hedges

  • Negation

  • Subjectivity

  • Negative_Emotion

  • Reasoning

  • Agreement

  • Second_Person

  • Adverb_Limiter

  • Disagreement

  • Acknowledgement

  • First_Person_Plural

  • For_Me

  • WH_Questions

  • YesNo_Questions

  • Bare_Command

  • Truth_Intensifier

  • Apology

  • Ask_Agency

  • By_The_Way

  • Can_You

  • Conjunction_Start

  • Could_You

  • Filler_Pause

  • For_You

  • Formal_Title

  • Give_Agency

  • Affirmation

  • Gratitude

  • Hello

  • Informal_Title

  • Let_Me_Know

  • Swearing

  • Reassurance

  • Please

  • Positive_Emotion

  • Goodbye

  • Token_count