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diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index b06ea04c..e8662a7a 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"A Light-Touch, One-Function Package": [[0, "a-light-touch-one-function-package"]], "Additional FeatureBuilder Considerations": [[1, "additional-featurebuilder-considerations"]], "Advanced Configuration Columns": [[1, "advanced-configuration-columns"]], "Aggregation Overview": [[1, "id2"]], "Analyzing First Percentage (%)": [[1, "analyzing-first-percentage"]], "Base Conversation-Level Features": [[11, "base-conversation-level-features"]], "Basic Input Columns": [[1, "basic-input-columns"]], "Certainty": [[30, null]], "Citation": [[29, "citation"], [30, "citation"], [31, "citation"], [32, "citation"], [33, "citation"], [34, "citation"], [35, "citation"], [36, "citation"], [37, "citation"], [38, "citation"], [40, "citation"], [41, "citation"], [42, "citation"], [43, "citation"], [44, "citation"], [45, "citation"], [46, "citation"], [47, "citation"], [48, "citation"], [49, "citation"], [50, "citation"], [51, "citation"], [52, "citation"], [53, "citation"], [54, "citation"], [55, "citation"], [56, "citation"], [57, "citation"], [58, "citation"], [59, "citation"], [60, "citation"]], "Configuring the FeatureBuilder": [[1, "configuring-the-featurebuilder"]], "Content Word Accommodation": [[31, null]], "Contents:": [[61, null]], "Conversation Parameters": [[1, "conversation-parameters"]], "Conversation-Level Aggregates": [[11, "conversation-level-aggregates"]], "Conversation-Level Features": [[11, "conversation-level-features"], [39, "conversation-level-features"]], "Conversational Repair": [[32, null]], "Cumulative Grouping": [[1, "cumulative-grouping"]], "Custom Aggregation": [[1, "custom-aggregation"]], "Custom Features": [[1, "custom-features"]], "Customizable Parameters": [[0, "customizable-parameters"]], "Dale-Chall Score": [[33, null]], "Declaring a FeatureBuilder": [[61, "declaring-a-featurebuilder"]], "Demo / Sample Code": [[0, "demo-sample-code"], [1, "demo-sample-code"]], "Discursive Diversity": [[34, null]], "Example Usage of Custom Aggregation Parameters": [[1, "example-usage-of-custom-aggregation-parameters"]], "Example:": [[41, "example"]], "FEATURE NAME": [[29, null]], "Feature Column Names": [[1, "feature-column-names"], [61, "feature-column-names"]], "Feature Documentation": [[62, "feature-documentation"]], "Feature Information": [[1, "feature-information"], [61, "feature-information"]], "Features: Conceptual Documentation": [[39, null]], "Features: Technical Documentation": [[11, null]], "Forward Flow": [[35, null]], "Function Word Accommodation": [[36, null]], "Generating Features: Utterance-, Speaker-, and Conversation-Level": [[62, "generating-features-utterance-speaker-and-conversation-level"]], "Getting Started": [[1, "getting-started"], [61, "getting-started"], [62, "getting-started"]], "Gini Coefficient": [[37, null]], "Hedge": [[38, null]], "High*Level Intuition": [[54, "high-level-intuition"]], "High-Level Intuition": [[29, "high-level-intuition"], [30, "high-level-intuition"], [31, "high-level-intuition"], [32, "high-level-intuition"], [33, "high-level-intuition"], [34, "high-level-intuition"], [35, "high-level-intuition"], [36, "high-level-intuition"], [37, "high-level-intuition"], [38, "high-level-intuition"], [40, "high-level-intuition"], [41, "high-level-intuition"], [42, "high-level-intuition"], [43, "high-level-intuition"], [44, "high-level-intuition"], [45, "high-level-intuition"], [46, "high-level-intuition"], [47, "high-level-intuition"], [48, "high-level-intuition"], [49, "high-level-intuition"], [50, "high-level-intuition"], [51, "high-level-intuition"], [52, "high-level-intuition"], [53, "high-level-intuition"], [55, "high-level-intuition"], [56, "high-level-intuition"], [57, "high-level-intuition"], [58, "high-level-intuition"], [59, "high-level-intuition"], [60, "high-level-intuition"]], "Implementation": [[32, "implementation"], [42, "implementation"], [52, "implementation"], [54, "implementation"]], "Implementation Basics": [[29, "implementation-basics"], [30, "implementation-basics"], [31, "implementation-basics"], [33, "implementation-basics"], [34, "implementation-basics"], [35, "implementation-basics"], [36, "implementation-basics"], [37, "implementation-basics"], [38, "implementation-basics"], [40, "implementation-basics"], [41, "implementation-basics"], [43, "implementation-basics"], [44, "implementation-basics"], [45, "implementation-basics"], [46, "implementation-basics"], [47, "implementation-basics"], [48, "implementation-basics"], [49, "implementation-basics"], [50, "implementation-basics"], [51, "implementation-basics"], [53, "implementation-basics"], [55, "implementation-basics"], [56, "implementation-basics"], [57, "implementation-basics"], [58, "implementation-basics"], [59, "implementation-basics"], [60, "implementation-basics"]], "Implementation Notes/Caveats": [[29, "implementation-notes-caveats"], [30, "implementation-notes-caveats"], [31, "implementation-notes-caveats"], [33, 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"import-recommendations-virtual-environment-and-pip"]], "Important Notes and Caveats": [[1, "important-notes-and-caveats"]], "Importing the Package": [[1, "importing-the-package"]], "Indices and Tables": [[61, "indices-and-tables"]], "Information Diversity": [[40, null]], "Information Exchange": [[41, null]], "Input File": [[34, "id2"]], "Inspecting Generated Features": [[1, "inspecting-generated-features"], [61, "inspecting-generated-features"]], "Interpretation:": [[41, "interpretation"]], "Interpreting the Feature": [[29, "interpreting-the-feature"], [30, "interpreting-the-feature"], [31, "interpreting-the-feature"], [32, "interpreting-the-feature"], [33, "interpreting-the-feature"], [34, "interpreting-the-feature"], [35, "interpreting-the-feature"], [36, "interpreting-the-feature"], [37, "interpreting-the-feature"], [38, "interpreting-the-feature"], [40, "interpreting-the-feature"], [41, "interpreting-the-feature"], [42, "interpreting-the-feature"], [43, 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"related-features"], [34, "related-features"], [35, "related-features"], [36, "related-features"], [37, "related-features"], [38, "related-features"], [40, "related-features"], [41, "related-features"], [42, "related-features"], [43, "related-features"], [44, "related-features"], [45, "related-features"], [46, "related-features"], [47, "related-features"], [48, "related-features"], [49, "related-features"], [50, "related-features"], [51, "related-features"], [52, "related-features"], [53, "related-features"], [54, "related-features"], [55, "related-features"], [56, "related-features"], [57, "related-features"], [58, "related-features"], [59, "related-features"], [60, "related-features"]], "Sentiment (RoBERTa)": [[51, null]], "Speaker Turn Counts": [[59, "id2"]], "Speaker- (User) Level Features": [[11, "speaker-user-level-features"]], "Table of Contents": [[61, "table-of-contents"]], "Team Burstiness": [[55, null]], "Textblob Polarity": [[56, null]], "Textblob Subjectivity": [[57, 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\ No newline at end of file
diff --git a/docs/build/html/utils/calculate_conversation_level_features.html b/docs/build/html/utils/calculate_conversation_level_features.html
index b377c9a3..df109253 100644
--- a/docs/build/html/utils/calculate_conversation_level_features.html
+++ b/docs/build/html/utils/calculate_conversation_level_features.html
@@ -55,10 +55,7 @@
Utilities
- “Driver” Classes: Utterance-, Conversation-, and Speaker-Level Features
@@ -92,181 +89,8 @@
-
-calculate_conversation_level_features module
-
--
-class utils.calculate_conversation_level_features.ConversationLevelFeaturesCalculator(chat_data: DataFrame, user_data: DataFrame, conv_data: DataFrame, vect_data: DataFrame, vector_directory: str, conversation_id_col: str, speaker_id_col: str, message_col: str, timestamp_col: str, convo_aggregation: bool, convo_methods: list, convo_columns: list, user_aggregation: bool, user_methods: list, user_columns: list, chat_features: list)
-Bases: object
-Initialize variables and objects used by the ConversationLevelFeaturesCalculator class.
-This class uses various feature modules to define conversation-level features. It reads input data and
-initializes variables required to compute the features.
-
-- Parameters:
-
-chat_data (pd.DataFrame) – Pandas dataframe of chat-level features read from the input dataset user_data (pd.DataFrame) – Pandas dataframe of user-level features derived from the chat-level dataframe conv_data (pd.DataFrame) – Pandas dataframe of conversation-level features derived from the chat-level dataframe vect_data (pd.DataFrame) – Pandas dataframe of processed vectors derived from the chat-level dataframe vector_directory (str) – Directory where vector files are stored convo_aggregation (bool) – If true, will aggregate features at the conversational level convo_methods (list) – Specifies which functions users want to aggregate with (e.g., mean, stdev…) convo_columns (list) – Specifies which columns (at the chat level) users want aggregated user_aggregation – If true, will aggregate features at the user level user_methods (list) – Specifies which functions users want to aggregate with (e.g., mean, stdev…) at the user level user_columns (list) – Specifies which columns (at the chat level) users want aggregated for the user level chat_features (list) – Tracks all the chat-level features generated by the toolkit
-
-
--
-calculate_conversation_level_features(feature_methods: list) → DataFrame
-Main driver function for creating conversation-level features.
-This function computes various conversation-level features by aggregating chat-level and user-level features,
-and appends them as new columns to the input conversation-level data.
-
-- Parameters:
-feature_methods – The list of methods to use to generate features
-- Returns:
-The conversation-level dataset with new columns for each conversation-level feature
-- Return type:
-pd.DataFrame
-
-
-
-
--
-calculate_info_diversity() → None
-Calculate an information diversity score for the team.
-This function computes the information diversity score by looking at the cosine
-similarity between the mean topic vector of the team and each message’s topic vectors,
-and merges the results into the conversation-level data.
-
-- Returns:
-None
-- Return type:
-None
-
-
-
-
--
-calculate_team_burstiness() → None
-Calculate the team burstiness coefficient.
-This function computes the team burstiness coefficient by looking at the differences
-in standard deviation and mean of the time intervals between chats, and merges the
-results into the conversation-level data.
-
-- Returns:
-None
-- Return type:
-None
-
-
-
-
--
-get_conversation_level_aggregates() → None
-Aggregate summary statistics from chat-level features to conversation-level features.
-This function calculates and merges the following aggregation functions for each summarizable feature:
-- Average/Mean
-- Standard Deviation
-- Minimum
-- Maximum
-For countable features (e.g., num_words, num_chars, num_messages), it also calculates and merges the sum.
-
-- Returns:
-None
-- Return type:
-None
-
-
-
-
--
-get_discursive_diversity_features() → None
-Calculate discursive diversity features for each conversation.
-This function computes discursive diversity based on the word embeddings (SBERT)
-and chat-level information, and merges the features into the conversation-level data.
-
-- Returns:
-None
-- Return type:
-None
-
-
-
-
--
-get_gini_features() → None
-Calculate the Gini index for relevant features in the conversation.
-This function computes the Gini index for features involving counts, such as:
-- Word count
-- Character count
-- Message count
-The Gini index is then merged into the conversation-level data.
-
-- Returns:
-None
-- Return type:
-None
-
-
-
-
--
-get_turn_taking_features() → None
-Calculate the turn-taking index in the conversation.
-This function merges turn-taking features into the conversation-level data.
-
-- Returns:
-None
-- Return type:
-None
-
-
-
-
--
-get_user_level_aggregates() → None
-Aggregate summary statistics from user-level features to conversation-level features.
-This function calculates and merges the following aggregation functions for each user-level feature:
-- Average/Mean of summed user-level features
-- Standard Deviation of summed user-level features
-- Minimum of summed user-level features
-- Maximum of summed user-level features
-- Average/Mean of averaged user-level features
-- Standard Deviation of averaged user-level features
-- Minimum of averaged user-level features
-- Maximum of averaged user-level features
-
-- Returns:
-None
-- Return type:
-None
-
-
-
-
-
+
+calculate_conversation_level_features module
diff --git a/docs/build/html/utils/calculate_user_level_features.html b/docs/build/html/utils/calculate_user_level_features.html
index a8a17750..3b57c3f2 100644
--- a/docs/build/html/utils/calculate_user_level_features.html
+++ b/docs/build/html/utils/calculate_user_level_features.html
@@ -56,10 +56,7 @@
- “Driver” Classes: Utterance-, Conversation-, and Speaker-Level Features
- Other Utilities
@@ -92,110 +89,8 @@
-
-calculate_user_level_features module
-
--
-class utils.calculate_user_level_features.UserLevelFeaturesCalculator(chat_data: DataFrame, user_data: DataFrame, vect_data: DataFrame, conversation_id_col: str, speaker_id_col: str, user_aggregation: bool, user_methods: list, user_columns: list, chat_features: list)
-Bases: object
-Initialize variables and objects used by the UserLevelFeaturesCalculator class.
-This class uses various feature modules to define user- (speaker) level features. It reads input data and
-initializes variables required to compute the features.
-
-- Parameters:
-
-chat_data (pd.DataFrame) – Pandas dataframe of chat-level features read from the input dataset user_data (pd.DataFrame) – Pandas dataframe of user-level features derived from the chat-level dataframe vect_data (pd.DataFrame) – Pandas dataframe of message embeddings corresponding to each instance of the chat data conversation_id_col (str) – A string representing the column name that should be selected as the conversation ID. Defaults to “conversation_num”. speaker_id_col (str) – A string representing the column name that should be selected as the speaker ID. Defaults to “speaker_nickname”. user_aggregation (bool) – If true, will aggregate features at the user level user_methods (list) – Specifies which functions users want to aggregate with (e.g., mean, stdev…) at the user level user_columns (list) – Specifies which columns (at the chat level) users want aggregated for the user level chat_features (list) – Tracks all the chat-level features generated by the toolkit
-
-
--
-calculate_user_level_features() → DataFrame
-Main driver function for creating user-level features.
-This function computes various user-level features by aggregating chat-level data,
-and appends them as new columns to the input user-level data.
-
-- Returns:
-The user-level dataset with new columns for each user-level feature
-- Return type:
-pd.DataFrame
-
-
-
-
--
-get_centroids() → None
-Calculate the centroid of each user’s chats in a given conversation for future discursive metric calculations.
-This function computes and appends the mean embedding (centroid) of each user’s chats to the user-level data.
-
-- Returns:
-None
-- Return type:
-None
-
-
-
-
--
-get_user_level_summary_statistics_features() → None
-This function is used to aggregate the summary statistics from
-chat level features to user level features.
-There are many possible ways to aggregate user level features, e.g.:
-- Mean of all chats by a given user;
-- Max of all chats by a given user;
-- Weighted mean (e.g., looking at different time points?)
-… and so on.
-This is an open question, so we are putting a TODO here.
-
-
-
--
-get_user_level_summed_features() → None
-Aggregate summary statistics from chat-level features that need to be summed together.
-Features for which summing makes sense include:
-- Word count (total number of words)
-- Character count
-- Message count
-This function calculates and merges the summed features into the user-level data.
-
-- Returns:
-None
-- Return type:
-None
-
-
-
-
--
-get_user_network() → None
-Get the user list per user per conversation.
-This function calculates and appends the list of other users in a given conversation to the user-level data.
-
-- Returns:
-None
-- Return type:
-None
-
-
-
-
-
+
+calculate_user_level_features module
diff --git a/docs/build/html/utils/check_embeddings.html b/docs/build/html/utils/check_embeddings.html
index e3a078df..d50ae20f 100644
--- a/docs/build/html/utils/check_embeddings.html
+++ b/docs/build/html/utils/check_embeddings.html
@@ -71,6 +71,7 @@
is_valid_term()load_liwc_dict()read_in_lexicons()sort_words()str_to_vec()
-
-utils.check_embeddings.is_valid_term(dicTerm)
+utils.check_embeddings.is_valid_term(dicTerm: str) → bool
Check if a dictionary term is valid.
-This function returns True if the term matches the regex pattern and False otherwise.
-The regex pattern matches:
-
-Alphanumeric characters (a-z, A-Z, 0-9) Valid symbols: -, ‘, *, / The * symbol can appear only once at the end of a word Emojis are valid only when they appear alone The / symbol can appear only once after alphanumeric characters Spaces are allowed between valid words
+This functions returns True if the term matches the regex pattern and Flase otherwise.
+The regex pattern matches:
+- Alphanumeric characters (a-zA-Z0-9)
+- Valid symbols: -, ‘, *, /
+- The * symbol can only appear once at the end of a word
+- 8 emojis are valid only when they appear alone
+- The / symbol can only appear once after alphanumeric characters
+- Spaces are allowed between valid words
- Parameters:
-dicTerm (str) – The dictionary term to validate.
+dicTerm (str) – The dictionary term
- Returns:
-True if the term is valid, False otherwise.
+
+hi:) 120
+
+- Returns:
+True/False
- Return type:
-bool
+- Return type:
+bool
@@ -308,7 +310,18 @@
This functions reads the content of a LIWC dictionary file in the official format,
and convert it to a dictionary with lexicon: regular expression format.
We assume the dicText has two parts: the header, which maps numbers to “category names,”
-and the body, which maps words in the lexicon to different category numbers, separated by a ‘%’ sign.
+and the body, which maps words in the lexicon to different category numbers, separated by ‘%’.
+Below is an example:
+‘’’
+%
+1 function
+2 pronoun
+3 ppron
+%
+again 1 2
+against 1 2 3
+‘’’
+Note that the elements in each line are separated by ‘ ‘.
- Parameters:
dicText (str) – The content of a .dic file
@@ -327,6 +340,25 @@
utils.check_embeddings.read_in_lexicons(directory, lexicons_dict)
+
+-
+utils.check_embeddings.sort_words(lexicons: list) → str
+Sorts the dictionary terms in a list.
+This function sorts the dictionary terms in a list by their length in descending order.
+The hyphenated words are sorted first, followed by the non-hyphenated words.
+
+- Parameters:
+dicTerms (list) – List of dictionary terms
+- Returns:
+dicTerms
+- Return type:
+str
+
+
+
-
utils.check_embeddings.str_to_vec(str_vec)
diff --git a/docs/source/features_conceptual/liwc.rst b/docs/source/features_conceptual/liwc.rst
index fb0c2d3b..04a4ff77 100644
--- a/docs/source/features_conceptual/liwc.rst
+++ b/docs/source/features_conceptual/liwc.rst
@@ -20,6 +20,14 @@ Positive Word Lexicon: `Hu and Liu (2004) |
