Caveats for Using ConversationAlign | Prepare your Transcripts Outside of the Package | Installation | Calibaration Transcripts Included in ConversationAlign | NurseryRhymes | Maron-Gross Interview | Caveat emptor | Background and Supporting Materials | References

Overarching functions of the package | What is semantic distance? | Distance Metric 1: Experiential | Distance Metric 2: Embedding | What kinds of questions can SemanticDistance answer? | Prepping my language sample for SemanticDistance | Prep Data Outside the Package | Install package and load library | Read your data into R | Clean, format, and run distances on your data

Reading data into R for ConversationAlign | Prepping your data for import | read_dyads() | read_1file()

Cleaning, Formatting, Aligning Norms to Your Data | Stopword removal | Lemmatization | Dimension Selection | prep_dyads() | Example of a prepped dataset

Generating sham conversations

Dialogues | Step 1: Clean Dialogue Transcript (clean_dialogue) | Step 2: Compute Semantic Distances | Dialogue Distance Turn-to-Turn (dist_dialogue)

Data Visualization Options | STEP 1: CLEAN AND FORMAT YOUR MONOLOGUE OR LIST | STEP 2: CREATE DENDROGRAM or NETWORK | Option 1: Hierarchical Cluster Dendrogram | Option 2: iGraph network

A typical monologue transcript | A typical unordered word list | Step 1: Clean Monologue or List: clean_monologue_or_list | Step 2: Choose Distance Option/Compute Distances | Option 1: Ngram-to-Word Distance: dist_ngram2word | Option 2: Ngram-to-Ngram Distance: dist_ngram2ngram | Option 3: Anchor-to-Word Distance: dist_anchor

Word Pairs | Clean Word Pairs in Columns Transcript | Word Pairs Semantic Distance