Emotion Dynamics

Emotion dynamics is a framework from Psychology for measuring how an individual’s emotional state changes over time (Hollenstein, 2015; Kuppens and Verduyn, 2017). Hipson and Mohammad (2020) introduced an analogous framework for changes in emotional state associated with one's utterances. They refer to it as Utterance Emotion Dynamics (UED). Specifically, they proposed a set of metrics (such as home base, density, variability, rise rate, etc.) that quantify various characteristics of change in one's emotional state as reflected in their utterances. This Python package allows the user to calculate these UED metrics from text.

To calculate the UED metrics one needs to specify:

1. Utterances by various speakers
2. Temporal information about the utterances, for e.g., time stamps associated with the utterances, or simply an ordering of utterances by time (even if the exact time of utterance is not known)
3. How to capture emotion states associated with the text

This package allows one to capture emotion states using features drawn from emotion lexicons such as the NRC VAD Lexicon. Rolling windows of words (moving forward one-word at a time) determine the sequence of emotional states. (UED metrics can be calculated using other forms of emotion features too, e.g., predictions of supervised emotion detection systems on individual utterances; however, that is not supported yet.) Further details about the code and input specifications can be found in the code folder of this repository.

If you use any of the resources provided in this repository, cite the following work:

    @inproceedings{VM2022-TED,
    title={Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canada},
    author={Krishnapriya Vishnubhotla and Saif M. Mohammad},
    booktitle={Proceedings of the Thirteenth International Conference on Language Resources and Evaluation (LREC 2022)},
    address={Marseille, France},
    year={2022}
    }

If you use the emotion dynamics package for your dataset, also cite the following work:

@article{hipson2021emotion,
doi = {10.1371/journal.pone.0256153},
author = {Hipson, Will E. AND Mohammad, Saif M.},
journal = {PLOS ONE},
publisher = {Public Library of Science},
title = {Emotion dynamics in movie dialogues},
year = {2021},
month = {09},
volume = {16},
url = {https://doi.org/10.1371/journal.pone.0256153},
pages = {1-19}

}

What does this repository provide?

This repository consists of two main submodules:

1. Code to run the Emotion Dynamics framework. The code has been designed to work with any temporally-ordered text data, including but not limited to tweets. See the Readme within the code folder for further instructions.

2. The TUSC tweet dataset described in the paper Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canada.

Where can you use the Emotion Dynamics framework?

The framework can be used to characterize the emotional state and trajectory of a speaker via their textual utterances over a period of time. Hipson and Mohammad apply it to analyze characters in a corpus of movie dialogues. The code folder of this repository shows a sample usage on character dialogue in literary novels. Our Tweet Dyanmics work applies it to individual tweeters -- one can do so with other data as well (e.g., posts from Reddit, self-reported diaries, political discussions, etc.).

Hipson and Mohammed (2021)

Hipson and Mohammad apply the UED framework to utterances from a corpus of movie dialogues, which are naturally ordered along a temporal axis. They represented emotional state in a two-dimensional valence--arousal space. The co-ordinates are determined by the average valence and arousal scores of the words (using the NRC VAD lexicon) in a small window of recent utterances (usually spanning 20 to 50 words). Rolling windows of words (moving forward one-word at a time) determine the sequence of emotional states. Here, we apply that framework to tweets. However, in this work we consider each of the valence, arousal, and dominance dimensions separately (separate one-dimensional axes). Their R code can be found at https://github.com/whipson/edyn.

What is Tweet Emotion Dynamics?

Tweet Emotion Dynamics, or TED, is a set of metrics that quantify the emotional characteristics of a set of tweets and tweeters. A large portion of the metrics are directly adapted from the Utterance Emotion Dynamics (UED) framework of Hipson and Mohammad.

These metrics can be broadly described by the following three questions:

1. On average, what is the emotion score of the tweets for a particular tweeter? (Section 4.1)
2. On average, how many tweets of a particular tweeter contain emotion-indicating words? (Section 4.2)
3. How can we quantify how the average emotionality of tweets varies over time, for a particular tweeter? (Utterance Emotion Dynamics; Section 5)

Folder Structure

• data/: Tweet IDs for tusc-city and tusc-country subsets. Each folder is split by city/country, and further by month. Each file contains the ID of the tweet.

• code/: Code for running the emotion dynamics and emotion word usage analyses.

  • clean_data.py: functions to tokenize tweets and identify URLs.
  • avgEmoValues.py: functions to obtain number of tokens per tweet, number of lexicon tokens per tweet, and the average emotion value of words in that tweet, given an emotion lexicon.
  • uedLib/: code to run emotion dynamics on tweet data.
    • run_ued.py: sample call to the main UED library.
    • lib/ued.py: main UED module that takes a config file as input and outputs emotion dynamic metrics.

• lexicons/: Files with words and associated values for different emotion dimensions.

  Note: Please see terms of use for these lexicons from their original source at http://saifmohammad.com/WebPages/lexicons.html. If you wish to run the emotion dynamics code with any of these lexicons, please download from the original source. The complete list of lexicons and their sources is described in the next section.

  This folder contains three main categories of lexicons for each of the valence-arousal-dominance dimensions:

  • <dim>.csv: The complete lexicon
  • <dim>_polar.csv: Does not contain words with a score between (0.33, 0.67).
  • <dim>_low.csv: Terms with scores <=0.33
  • <dim>_high.csv: Terms with scores >=0.67

  All lexicon files have two columns: word and val.

Lexicons

The following lexicons are provided for ease of running the code in the lexicons/ folder, but must be downloaded directly from the associated sources:

1. The valence, arousal, and dominance lexicons

   These are sourced from the NRC-VAD Lexicon. See terms of use at the bottom of the linked page. Cite the following work if you use these lexicons:

    @inproceedings{vad-acl2018,
    title={Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words},
    author={Mohammad, Saif M.},
    booktitle={Proceedings of The Annual Conference of the Association for Computational Linguistics (ACL)},
    year={2018},
    address={Melbourne, Australia}
    }
   

2. Lexicons for the eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, disgust) and two sentiments (negative and positive)

   These are sourced from the NRC Word-Emotion Association Lexicon, or EmoLex. Download the lexicons by going to the linked page and selecting the appropriate usage license. Cite the following works if you use these lexicons:

    @article{Mohammad13,
    Author = {Mohammad, Saif M. and Turney, Peter D.},
    Journal = {Computational Intelligence},
    Number = {3},
    Pages = {436--465},
    Title = {Crowdsourcing a Word-Emotion Association Lexicon},
    Volume = {29},
    Year = {2013}
    }
   
    @inproceedings{mohammad-turney-2010-emotions,
    title = "Emotions Evoked by Common Words and Phrases: Using {M}echanical {T}urk to Create an Emotion Lexicon",
    author = "Mohammad, Saif  and
    Turney, Peter",
    booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text",
    month = jun,
    year = "2010",
    address = "Los Angeles, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W10-0204",
    pages = "26--34",
    }
   

Ethical Considerations

Any analysis involving the use of emotion and sentiment lexicons must be preceded by a consideration of the relevant ethical ramifications, and the intended as well as potential uses of the associated results and tools. See the papers below:

1. Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis: for fifty ethical considerations relevant to the use of Automatic Emotion Recognition (AER) systems.

2. Ethics Sheets for AI Tasks: for a more general framework, and ethics sheet, for thinking about the ethical consideratios involved in any AI task.

3. Practical and Ethical Considerations in the Effective use of Emotion and Sentiment Lexicons: for specific tips and considerations in the use of emotion lexicons.

Authors

For any queries, you can contact the authors:

• Krishnapriya Vishnubhotla (University of Toronto)
• Saif M. Mohammad (National Research Council Canada)

Contact: [email protected], [email protected]