Features: Technical Documentation

Below is a list of the features currently built and documented within our toolkit. We describe the different levels of analysis for features in the Introduction, under Generating Features: Utterance-, Speaker-, and Conversation-Level.

Utterance- (Chat) Level Features

Utterance-Level features are calculated first in the Toolkit, as many conversation-Level features are derived from utterance-level information. These are the basic attributes that can be used to describe a single message (“utterance”) in a conversation.

• basic_features module
• certainty module
• lexical_features_v2 module
• other_lexical_features module
• info_exchange_zscore module
• question_num module
• politeness_features module
• hedge module
• temporal_features module
• readability module
• textblob_sentiment_analysis module
• named_entity_recognition_features module
• politeness_v2 module
• politeness_v2_helper module
• reddit_tags module
• word_mimicry module
• fflow module

Conversation-Level Features

Base Conversation-Level Features

The following features are constructs that are defined only at the conversation-level, such as the level of “burstiness” in a team’s communication patterns. We call these the “base” conversation-level features, and they can be accessed using a property of the FeatureBuilder object: FeatureBuilder.conv_features_base.

• burstiness module
• information_diversity module
• gini_coefficient module
• get_all_DD_features module
• discursive_diversity module
• variance_in_DD module
• within_person_discursive_range module
• turn_taking_features module

Conversation-Level Aggregates

Once utterance-level features are computed, we compute conversation-level features; some of these features represent an aggregation of utterance-level information (for example, the “average level of positivity” in a conversation is simply the mean positivity score for each utterance).

By default, all numeric attributes generated at the utterance (chat) level are aggregated using the functions mean, max, min, and stdev. However, this behavior can be customized, with details in the Worked Example (see Custom Aggregation).

Speaker- (User) Level Features

User-level features generally represent an aggregation of features at the utterance- level (for example, the average number of words spoken by a particular user). There is therefore limited speaker-level feature documentation, other than a function used to compute the “network” of other speakers that an individual interacts with in a conversation.

You may reference the Speaker (User)-Level Features Page for more information, as well as the details in the Worked Example (see Custom Aggregation).

• get_user_network module