ALIGN, a computational tool for multi-level language analysis (optimized for Python 3.10)

align is a Python library for extracting quantitative, reproducible metrics of multi-level alignment between two speakers in naturalistic language corpora. The method was introduced in "ALIGN: Analyzing Linguistic Interactions with Generalizable techNiques" (Duran, Paxton, & Fusaroli, 2019; Psychological Methods).

Installation

align may be downloaded directly using pip.

To download the stable version released on PyPI:

pip install align

To download directly from our GitHub repo:

pip install git+https://github.com/nickduran/align-linguistic-alignment.git

Quick documentation

ALIGN consists of two primary modules for conducting analyses, prepare_transcripts and calculate_alignment. To get a quick glance of the functions contained within each module, please check out the following:

• prepare_transcripts: https://nickduran.github.io/align-linguistic-alignment/prepare_transcripts.html

• calculate_alignment: https://nickduran.github.io/align-linguistic-alignment/calculate_alignment.html

Additional tools required for some align options

The Google News pre-trained word2vec vectors (GoogleNews-vectors-negative300.bin) and the Stanford part-of-speech tagger (stanford-postagger-full-2020-11-17) are required for some optional align parameters but must be downloaded separately.

• Google News: https://code.google.com/archive/p/word2vec/ (page) or https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing (direct download)

• Stanford POS tagger: https://nlp.stanford.edu/software/tagger.shtml#Download (page) or https://nlp.stanford.edu/software/stanford-tagger-4.2.0.zip (direct download)

Tutorials

We created Jupyter Notebook tutorials to provide an easily accessible step-by-step walkthrough on how to use align. Below are descriptions of the current tutorials that can be found in the examples directory within this repository. If unfamiliar with Jupyter Notebooks, instructions for installing and running can be found here: http://jupyter.org/install. We recommend installing Jupyter using Anaconda. Anaconda is a widely-used Python data science platform that helps streamline workflows. A major advantage is that Anaconda also makes it easy to set up unique Python environments - which may be necessary to run align and the tutorials given align is currently optimized for Python 3.

• Jupyter Notebook 1: CHILDES

  • This tutorial walks users through an analysis of conversations from a single English corpus from the CHILDES database (MacWhinney, 2000)---specifically, Kuczaj’s Abe corpus (Kuczaj, 1976). We analyze the last 20 conversations in the corpus in order to explore how ALIGN can be used to track multi-level linguistic alignment between a parent and child over time, which may be of interest to developmental language researchers. Specifically, we explore how alignment between a parent and a child changes over a brief span of developmental trajectory.

• Jupyter Notebook 2: Devil's Advocate

  • This tutorial walks users throught the analysis reported in (Duran, Paxton, & Fusaroli, 2019). The corpus consists of 94 written transcripts of conversations, lasting eight minutes each, collected from an experimental study of truthful and deceptive communication. The goal of the study was to examine interpersonal linguistic alignment between dyads across two conversations where participants either agreed or disagreed with each other (as a randomly assigned between-dyads condition) and where one of the conversations involved the truth and the other deception (as a within-subjects condition).

We are in the process of adding more tutorials and would welcome additional tutorials by interested contributors.

Attribution

If you find the package useful, please cite our manuscript:

Duran, N., Paxton, A., & Fusaroli, R. (2019). ALIGN: Analyzing Linguistic Interactions with Generalizable techNiques. Psychological Methods. http://dynamicog.org/papers/

Licensing of example data

• CHILDES

  • Example corpus "Kuczaj Corpus" by Stan Kuczaj is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License (https://childes.talkbank.org/access/Eng-NA/Kuczaj.html):

  Kuczaj, S. (1977). The acquisition of regular and irregular past tense forms. Journal of Verbal Learning and Verbal Behavior, 16, 589–600.

• Devil's Advocate

  • The complete de-identified dataset of raw conversational transcripts is hosted on a secure protected-access repository provided by the Inter-university Consortium for Political and Social Research (ICPSR). Please click on the link to access: http://dx.doi.org/10.3886/ICPSR37124.v1. Due to the requirements of our IRB, please note that users interested in obtaining these data must complete a Restricted Data Use Agreement, specify the reason for the request, and obtain IRB approval or notice of exemption for their research.

  Duran, Nicholas, Alexandra Paxton, and Riccardo Fusaroli. Conversational Transcripts of Truthful and Deceptive Speech Involving Controversial Topics, Central California, 2012. ICPSR37124-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2018-08-29.