Hiram Ring
  • Posts
  • Music
    • Projects
    • Downloads
    • Bio/Press
    • Music Photos
    • Music Links
    • Store
  • Linguistics
    • Travel Photos
    • Useful Linguistic Links
  • Posts
  • Music
    • Projects
    • Downloads
    • Bio/Press
    • Music Photos
    • Music Links
    • Store
  • Linguistics
    • Travel Photos
    • Useful Linguistic Links

Automating the coding of implicit motives (Paper announcement)

5/14/2020

0 Comments

 
The past week has been quite exciting, as we've been in the final stages of preparing a final manuscript on machine learning. I've been rather quiet about this collaboration since the end of 2017, what with the new focus of the project in Zurich, a growing family, and the general pressures of life, not to mention the difficulty of developing a text classifier using neural networks. This particular classification problem is made harder by the fact that motive content is much more diverse in its linguistic manifestation than, for example, sentiment (positive/negative polarity).

This equal-authored paper, however, marks the first phase of what we hope will be a long-running and ultimately successful collaborative research focus on developing a neural network model that can automate the time-consuming nature of assessing implicit motive imagery (a personality measure) in written text. If you're interested in reading about it (we've tried to make the research fields of Implicit Motives, Machine Learning, and Natural Language Processing somewhat accessible), the citation for the online publication is below. And if you follow the URL link, you should be able to read the paper in its entirety (without needing a Springer access account).
  • Pang, Joyce S. & Hiram Ring. 2020. Automated coding of implicit motives: A machine-learning approach. Motivation and Emotion, pp. 1–19. doi:10.1007/s11031-020-09832-8. URL https://rdcu.be/b38pm.
If you're interested in checking out how a text classifier for implicit motives might work, you can visit the web app that we built, which uses an underlying CNN model for classification - keep in mind that not all text content has implicit motive imagery, and that this classifier does not yet perform classifications on par with trained human coders. However, it is a start. I may highlight some of the niceties that this particular neural network model can/can't handle in a future post.

In addition to the paper and links above, we provide trained models, data, and some dataset descriptives on the Open Science Framework website, which can be cited and linked to below.
  • Pang, Joyce S. & Hiram Ring. 2020. Automating implicit motive coding: Replication data and descriptives. OSF. doi:10.17605/OSF.IO/AURWB. URL https://osf.io/aurwb/.
I am hoping to write up an even more accessible summary of the paper in the coming weeks, but we'll see how it goes. Since the family is currently under COVID 'lockdown' in Singapore with two kids under 2, the only opportunities for doing anything not 'kid-wrangling' related come when the kids are sleeping, which is also when parents have to eat/shower/clean and do housework. This doesn't leave much time for anything else, which is another reason it was exciting to have finished the paper!
0 Comments



Leave a Reply.

    About me

    I'm a linguist and singer-songwriter. I write about life, travel, language and technology.

    Archives

    January 2022
    May 2020
    September 2019
    July 2018
    February 2018
    December 2017
    August 2017
    June 2017
    May 2017
    April 2017
    March 2017
    February 2017
    December 2015
    May 2015
    December 2014
    November 2014
    October 2014
    September 2014
    August 2014
    July 2014
    June 2014
    April 2014
    March 2014
    December 2013
    October 2013
    August 2013
    July 2013
    June 2013
    May 2013
    April 2013
    March 2013
    February 2013
    January 2013

    Categories

    All
    3mt
    Abbi
    Acoustic
    Advice
    AI
    Album
    All I Want
    Analysis
    Andaman
    Annotation
    Archive
    Audio
    Austroasiatic
    Backup
    Biate
    Bibliography
    Breathe Deep
    China
    Chords
    Clause Similarity
    Cloud
    Collaboration
    Computers
    Conference
    Culture
    Data
    Data Access
    Datasets
    DataVerse
    Death
    Deixis
    Demonstratives
    Documentation
    Draw
    Duration
    DX
    E920
    Easter
    El Capitan
    E Reader
    E-reader
    Examples
    EXcel
    F0
    Failure
    Feature
    Fieldwork
    Formants
    Forums
    Friends
    Ghana
    Git
    Git Metaphor
    Greet The Dawn
    Hanvon
    HLS20
    Holiday
    Home
    How-to
    ICAAL
    Implicit Motives
    Instruction
    Intensity
    Interlinear
    I've Got A Girl
    Kindle
    Language
    LaTeX
    Linguistics
    LyX
    Mac
    Machine Learning
    Mastering
    Metaphor
    MU
    Myanmar
    Natural Language Processing
    Neural Networks
    New Release
    News
    NLP
    NLTK
    Open Science
    Papers
    Paperwhite
    Pdf
    PhD
    Phonetics
    Phonology
    Pitch
    Plot
    Pnar
    Praat
    Practical
    Process
    Processing
    Production
    Programming
    Psalms
    Psychology
    Publications
    Publicity
    Python
    Radar Radio
    Reasons
    Recording
    Research
    Review
    Scripts
    Sentiment Analysis
    Singapore
    Song
    Soundfarm
    Sports
    Studio
    Subrepo
    Syntactic Reconstruction
    Text Classification
    Thailand
    Thesis
    Things To Know
    This Lamp
    Thoughts
    Tips
    Tone
    Toolbox
    Tools
    Track List
    Transcriber
    Transcriber 1.5.2
    Transcription
    Travel
    Trs2txt
    Update
    USA
    UZH
    Valentine's Day
    Version Control
    Video
    Vowels
    Web App
    Website
    Wedding
    Word - Flesh
    Workflow
    World Cup
    Writing
    YUFL
    Zion's Walls
    Zurich

    RSS Feed

    prev. blog

      Contact me

    Submit
Powered by Create your own unique website with customizable templates.