A good conversation is a cooperative endeavor where both parties
modify the form and content of their own production to align with each
other. This is a phenomenon known as alignment. People align across many
dimensions including the words they choose and the affective tenor of
their prosody. ConversationAlign measures dynamics of
lexical use between conversation partners across more than 40 semantic,
lexical, phonological, and affective dimensions. Before launching into
your analyses, there are some important use caveats to consider.
ConversationAlign only works on dyadic language
transcripts (i.e., 2-person dialogues).ConversationAlign does NOT parse turns automatically.
The software will aggregate all words produced by one speaker across
sentences and rows until a switch occurs in the ‘speaker’ column.ConversationAlign will strip punctuation and other
special characters automatically.ConversationAlign will split/vectorize your text into a
one-word-per-row format, retaining all variable labels.ConversationAlign will retain all meta-data throughout
text processing (e.g., timestamps, grouping variables).
ConversationAlign is pretty good at detecting and repairing
unconventional font encoding systems, but it will not catch everything,
You will find all sorts of hidden junk when you copy/paste interview
transcripts from random websites are from YouTube. Inspect your
transcripts to make sure they are what you think they are before
launching into a complex computational analysis.*.txt, *.csv, or Otter
*.ai).ConversationAlign. Install and load the development version of
ConversationAlign from GitHub using the devtools
package.
ConversationAlignConversationAligncontains two sample conversation
transcripts that are pre-load when you call the package. These are:
MaronGross_2013: Interview transcript of Marc Maron and
Terry Gross on NPR (2013).
NurseryRhymes: Three
nursery rhymes looping same phrases formatted as conversations, cleaned,
and aligned to illustrate how the formatting pipeline reshaopes
conversation transcripts.
| Event_ID | Participant_ID | Text_Raw |
|---|---|---|
| ItsySpider | Yin | The itsy-bitsy spider climbed up the water spout |
| ItsySpider | Maya | Down came the rain and washed the spider out |
| ItsySpider | Yin | Out came the sun, and dried up all the rain |
| ItsySpider | Maya | And the itsy-bitsy spider climbed up the spout again |
| ItsySpider | Yin | The itsy-bitsy spider climbed up the water spout |
| ItsySpider | Maya | Down came the rain and washed the spider out |
| ItsySpider | Yin | Out came the sun, and dried up all the rain |
| ItsySpider | Maya | And the itsy-bitsy spider climbed up the spout again |
| ItsySpider | Yin | The itsy-bitsy spider climbed up the water spout |
| ItsySpider | Maya | Down came the rain and washed the spider out |
| ItsySpider | Yin | Out came the sun, and dried up all the rain |
| ItsySpider | Maya | And the itsy-bitsy spider climbed up the spout again |
| ItsySpider | Yin | The itsy-bitsy spider climbed up the water spout |
| ItsySpider | Maya | Down came the rain and washed the spider out |
| ItsySpider | Yin | Out came the sun, and dried up all the rain |
| ItsySpider | Maya | And the itsy-bitsy spider climbed up the spout again |
| ItsySpider | Yin | The itsy-bitsy spider climbed up the water spout |
| ItsySpider | Maya | Down came the rain and washed the spider out |
| ItsySpider | Yin | Out came the sun, and dried up all the rain |
| ItsySpider | Maya | And the itsy-bitsy spider climbed up the spout again |
str(NurseryRhymes)
#> 'data.frame': 228 obs. of 3 variables:
#> $ Event_ID : chr "ItsySpider" "ItsySpider" "ItsySpider" "ItsySpider" ...
#> $ Participant_ID: chr "Yin" "Maya" "Yin" "Maya" ...
#> $ Text_Raw : chr "The itsy-bitsy spider climbed up the water spout" "Down came the rain and washed the spider out" "Out came the sun, and dried up all the rain" "And the itsy-bitsy spider climbed up the spout again" ...Here’s one from a 2013 NPR interview (USA) between Marc Maron and
Terry Gross, titled Marc Maron: A Life
Fueled By ‘Panic And Dread’.
| speaker | text |
|---|---|
| MARON | I’m a little nervous but I’ve prepared I’ve written things on a piece of paper |
| MARON | I don’t know how you prepare I could ask you that - maybe I will But this is how I prepare - I panic |
| MARON | For a while |
| GROSS | Yeah |
| MARON | And then I scramble and then I type some things up and then I handwrite things that are hard to read So I can you know challenge myself on that level during the interview |
| GROSS | Being self-defeating is always a good part of preparation |
| MARON | What is? |
| GROSS | Being self-defeating |
| MARON | Yes |
| GROSS | Self-sabotage |
| MARON | Yes |
| GROSS | Key |
| MARON | Right so you do that? |
| GROSS | I sometimes do that |
| MARON | How often? |
| GROSS | I try not to do that I do that more in life than I do in radio |
| MARON | Really? |
| GROSS | Yeah |
| MARON | Like today? |
| GROSS | Life is harder than radio |
str(MaronGross_2013)
#> 'data.frame': 546 obs. of 2 variables:
#> $ speaker: chr "MARON" "MARON" "MARON" "GROSS" ...
#> $ text : chr " I'm a little nervous but I've prepared I've written things on a piece of paper" " I don't know how you prepare I could ask you that - maybe I will But this is how I prepare - I panic" " For a while" " Yeah" ...Any analysis of language comes with assumptions and potential bias.
For example, there are some instances where a researcher might care
about morphemes and grammatical elements such as ‘the’, ‘a’, ‘and’,
etc.. The default for ConversationAlign is to omit these as stopwords
and to average across all open class words (e.g., nouns, verbs) in each
turn by interlocutor. There are some specific cases where this can all
go wrong. Here are some things to consider:
Stopwords :
ConversationAlign omits stopwords by default applying a
customized stopword list, Temple_Stopwords25. CLICK HERE to inspect the list. This
stopword list includes greetings, idioms, filler words, numerals, and
pronouns.
Lemmatization : The package will
lemmatize your language transcripts by default. Lemmatization transforms
inflected forms (e.g., standing, stands) into their root or dictionary
entry (e.g., stand). This helps for yoking offline values (e.g.,
happiness, concreteness) to each word and also entails what NLP folks
refer to as ‘term aggregation’. However, sometimes you might NOT want to
lemmatize. You can easily change this option by using the argument,
“lemmatize=FALSE,” to the clean_dyads function below.
Sample Size Issue 1: Exchange
Count: The program derives correlations and AUC for each dyad as
metrics of alignment. For very brief conversations (<30 turns), the
likelihood of unstable or unreliable estimates is high.
Sample Size Issue 2 : matching to
lookup database: ConversationAlign works by yoking values from a lookup
database to each word in your language transcript. Some variables have
lots of values characterizing many English words. Other variables (e.g.,
age of acquisition) only cover about 30k words. When a word in your
transcript does not have a ‘match’ in the lookup datase,
ConversationAlign will return an NA which will not go into the average
of the words for that interlocutor and turn. This can be dangerous when
there are many missing values. Beware!
Compositionality :
ConversationAlign is a caveman in its complexity. It matches a value to
each word as if that word is an island. Phenomena like polysemy (e.g.,
bank) and the modulation of one word by an intensifier (e.g., very
terrible) are not handled. This is a problem for many of the affective
measures but not for lexical variables like word length.
Preprint
Our PsyArXiv preprint describing
the method(s) in greater detail is referenced as: Sacks, B., Ulichney,
V., Duncan, A., Helion, C., Weinstein, S., Giovannetti, T., … Reilly, J.
(2025, March 12). ConversationAlign: Open-Source Software for
Analyzing Patterns of Lexical Use and Alignment in Conversation
Transcripts. Click Here to read
our preprint. It was recently invited for revision at Behavior Rsearch
Methods. We will update when/if eventually accepted there!
Methods for creating internal lookup database
ConversationAlign contains a large, internal lexical
lookup_database. Click
Here to see how we created this by merging other offline
psycholinguistic databases into one.
Variable Key for ConversationAlign
ConversationAlign currently allows users to compute alignment dynamics
across >40 different lexical, affective, and semantic dimensions.Click
Here to link to a variable key.
Lewis, David D., et al. (2004) “Rcv1: A new benchmark collection for text categorization research.” Journal of machine learning research 5: 361-397.