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# A Conversational Analysis Framework for Predicting Team Success through Team Communication Processes | ||
Our project seeks to answer the question, “which types of team communication processes matter in different team activities?” Depending on the type of task at hand (Straus, 1999) or the context of the research study (Levinthal & Rosenkopf, 2020), some strategies may be more useful than others. Thus, how might we synthesize the myriad studies of team behavior — which take place across many tasks and contexts — into actionable insights for managers? In this project, we will extract team communication processes from the organizational behavior literature (for example, “Positivity,” “Information Exchange,” “Equal Participation”), and then measure these features on real teams’ communication transcripts across a variety of tasks. We will then use these measured features to predict which types of communication are most associated with team success across different activities. | ||
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## Getting Started | ||
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If you are new to this repository, welcome! Please follow the steps below to get started. | ||
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### Step 1: Clone the Repo | ||
First, clone this repository into your local development environment: | ||
``` | ||
git clone https://github.com/Watts-Lab/team-process-map.git | ||
``` | ||
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### Step 2: Download Dependencies | ||
Second, we *strongly* recommend using a virtual environment to install the dependencies required for the project.= | ||
The dependencies of the project are listed in `feature_engine/requirements.txt`: https://github.com/Watts-Lab/team-process-map/blob/main/feature_engine/requirements.txt | ||
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**Python Version**: We recommend `python3.11` when running this repository. | ||
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Later versions of Python (e.g., 3.12) are currently incompatible with the `sentence-transformers` library. There is an open issue here: https://github.com/google/sentencepiece/issues/968 [Updated as of January 30, 2024] | ||
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#### Run Initial Scripts for Dependencies | ||
Before starting the featurizer, you need to run the following to obtain dependencies for the project: | ||
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``` | ||
python3 -m spacy download en_core_web_sm | ||
``` | ||
``` | ||
import nltk | ||
nltk.download('nps_chat') | ||
nltk.download('punkt') | ||
``` | ||
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### Step 3: Add "Hidden" Files | ||
Certain files associated with the project are "hidden" from the public GitHub repo at this time, for | ||
reasons of copyright, research embargo, or other requests. | ||
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These are instead saved on our private Google Drive. To access them, click this link: https://drive.google.com/drive/folders/1c-g4d-Pq6kT2el4oaCSiQGsQrtgd3VH3?usp=drive_link | ||
This will take you to a folder called "GitHub Assets." Then do the following: | ||
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1. Unzip the folder "lexicons.zip" | ||
2. Replace the folder under `feature_engine/features/lexicons` with the one in the ZIP file. | ||
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### Step 4: Initialize the data repository | ||
Test data for the project lives in a separate repository. To populate the data repository when running for the first time, run the following command: | ||
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``` | ||
git submodule update --init --recursive | ||
``` | ||
For more information on updating submodules, refer to [this documentation](https://stackoverflow.com/questions/1030169/pull-latest-changes-for-all-git-submodules). | ||
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### Step 5: Run the Featurizers | ||
At this point, you should be ready to run the featurizer! Navigate to the `feature_engine` folder, and use the following command: | ||
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``` | ||
python3 featurize.py | ||
``` | ||
This calls the `featurizer.py` file, which declares a FeatureBuilder object for different dataset of interest, and featurizes them using our framework. | ||
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## Documents and Handy Links | ||
- Our Team Email: [email protected] (Ask Emily for the password!) | ||
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- Our "master sheet": | ||
- Our "master sheet," where we : | ||
https://docs.google.com/spreadsheets/d/1JnChOKFXkv944LvnYbzI1qrHLEPfCvEMN5XzP1AxvmA/edit?usp=sharing | ||
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## Database of Current Communication Features | ||
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