[PRE REVIEW]: EmTract: A Python Package for Extracting Emotions from Social Media for Finance Research

[editorialbot]

Submitting author: @dvamossy (Domonkos Vamossy)
Repository: https://github.com/dvamossy/EmTract
Branch with paper.md (empty if default branch):
Version: v1.0.0
Editor: Pending
Reviewers: Pending
Managing EiC: Olivia Guest

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[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.88  T=0.02 s (568.4 files/s, 73626.5 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                           4             93             99            383
Jupyter Notebook                 3              0            569            215
Markdown                         3             67              0            174
TeX                              1              4              0             33
Bourne Shell                     1              3              0             21
YAML                             1              1              4             18
-------------------------------------------------------------------------------
SUM:                            13            168            672            844
-------------------------------------------------------------------------------


gitinspector failed to run statistical information for the repository

[editorialbot]

Wordcount for paper.md is 805

[editorialbot]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- None

MISSING DOIs

- 10.2139/ssrn.4384573 may be a valid DOI for title: Social Media Emotions and IPO Returns
- 10.2139/ssrn.3626025 may be a valid DOI for title: Investor emotions and earnings announcements

INVALID DOIs

- None

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[editorialbot]

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[oliviaguest]

@dvamossy thank you for choosing JOSS; can you fix the DOIs if applicable, please? ☺️

[oliviaguest]

@editorialbot query scope

[editorialbot]

Submission flagged for editorial review.

[oliviaguest]

@danasolav I am just going to check with my co-editors if this is substancially a contribution for the journal. Feel free to explain on this angle too if you want here.

[danasolav]

@oliviaguest I am not the related to this submission. Did you want to tag someone else?

[dvamossy]

I am happy to explain. The development of EmTract is motivated by the critical need for precise sentiment analysis in financial research, where the standard tools have been demonstrably inadequate, as detailed in the appendix of our foundational paper. For instance, existing sentiment analysis tools like VADER perform poorly in analyzing emotions within financial social media data. EmTract is designed to address the nuanced requirements of financial research, offering a more accurate and context-aware analysis compared to traditional, lexicon-based methods, which are still widely used. The paper presents significant improvements over other existing open-source emotion models. The open-source nature of EmTract aligns with the Journal of Open Source Software's (JOSS) mission, promoting transparency, collaboration, and the advancement of research tools accessible to all. Our package also includes a dataset of 10,000 hand-tagged financial social media posts.

Our aim in publishing EmTract in JOSS is twofold: to provide the finance research community with a tool that genuinely meets their specific needs, and to contribute to the broader open-source ecosystem, encouraging innovation and improvement. The increasing engagement from finance researchers and the potential for EmTract to become an important tool in the field underscores its significance and the substantial contribution it represents for JOSS and the wider research community.

[oliviaguest]

@dvamossy I am really sorry to bring you bad news, but for the scope of the journal we believe this is not currently a fit. As you can also see from the analysed stats above, 383 lines of Python code is too small for JOSS. Notwithstanding, the provided dataset is a significant resource, but it does not amount to research software under JOSS requirements. In addition, this paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3975884, provides adequate citation opportunities. So publishing in JOSS is contraindicated.

We wish you the best and while this may not be the news you wanted to hear, we do hope you consider JOSS when a piece of software needs peer feedback and a citation home in the future.

[oliviaguest]

@editorialbot reject

[editorialbot]

Paper rejected.