releasetrain-sentiment

Scores sentiment per comment in a Reddit discussion thread, flagging author replies vs. community responses, to measure sentiment divergence over time.

Main developer: manumathewjiss

Install

pip install releasetrain-sentiment

Two optional extras:

pip install releasetrain-sentiment[classify]   # TPS/GDS classifier (scikit-learn)
pip install releasetrain-sentiment[fetch]      # live ReleaseTrain API wrapper (requests)

Quickstart: scoring a thread

from releasetrain_sentiment import score_post, trajectory_summary

post = {
    "title": "Update broke my build",
    "description": "Nothing compiles since 3.2.0",
    "comments": [
        {"text": "Known issue, fix coming soon", "author": "dev1", "is_author": True},
        {"text": "Same here, very annoying", "author": "user2", "is_author": False},
        {"text": "Thanks for the heads up", "author": "dev1", "is_author": True},
    ],
}

results = score_post(post)
# results[0] is the post itself (source="post", index=-1)
# results[1:] is one entry per comment, same order as the input,
# each with compound/pos/neu/neg/label and is_author

summary = trajectory_summary(results)
# {"author_avg": ..., "community_avg": ..., "author_shift": ..., "community_shift": ...}

score_posts(list_of_posts) runs the same thing over a batch, returning one results array per input post, same order.

Command-line interface

Installed alongside the library as the releasetrain-sentiment command (also runnable as python -m releasetrain_sentiment). Each subcommand is a thin wrapper around the same function shown above, one-to-one, so the CLI and the Python API never drift in behavior from each other.

releasetrain-sentiment --help

Score a post, reading JSON from a file:

releasetrain-sentiment score --input thread.json --pretty

...or piped in from stdin (a JSON array of posts also works, and prints one {"items": ..., "trajectory": ...} object per post):

echo '{"title": "Update broke my build", "description": "Nothing compiles since 3.2.0", "comments": [{"text": "Known issue, fix coming soon", "author": "dev1", "is_author": true}, {"text": "Same here, very annoying", "author": "user2", "is_author": false}]}' | releasetrain-sentiment score

Check the keyword-contamination issue Reviewer #7 described, directly from the command line:

releasetrain-sentiment keyword-check "That redesign was a massive error" "I am so tired of this company"
# True    That redesign was a massive error
# False   I am so tired of this company

Print both the standard VADER label and the technical_first baseline label for one or more texts:

releasetrain-sentiment label "I am so tired of this company"
# I am so tired of this company
#   vader=Negative  technical_first=GDS

Classify text(s) with a model you've already trained and saved with save_classifier() (requires pip install releasetrain-sentiment[classify]):

releasetrain-sentiment classify --model tps_gds_model.joblib "the app crashed again"

Pull recent posts from a live ReleaseTrain instance and convert them to this package's scoreable shape in one step (requires pip install releasetrain-sentiment[fetch]):

releasetrain-sentiment fetch --limit 20 --out posts.json
releasetrain-sentiment score --input posts.json --pretty

Modules

• score.py: the scorer above. Pure function, no network calls, no ReleaseTrain coupling. Standard VADER thresholds (compound >= 0.05 positive, <= -0.05 negative).
• baseline_vader.py: a reconstruction of the paper's "technical_first" VADER baseline described in Reviewer #7's feedback (a keyword exemption plus a narrower +/- 0.15 neutral band). This is a reconstruction from the reviewer's written description, not the original experiment code, which was not available when this package was built. See the module docstring before citing it as a faithful reproduction.
• classify.py: a trainable TF-IDF + Multinomial Naive Bayes pipeline (Reviewer #9: "Multinomial Naive Bayes achieves the best balance in identifying the minority TPS class"). No pretrained model ships with this package; train it on your own labeled data.
• corpus.py: the corpus and labeling documentation reviewers #4, #7, and #9 each independently asked for (which subreddits/timeframes, what API, how labeled). ENHANCED_AUTOMATED_CORPUS is filled in from real, verified data; TPSGDS542_CORPUS has placeholder fields that need the actual gold-dataset values filled in before citing it in the paper.
• evaluate.py: sample_high_divergence() implements Reviewer #4's suggested audit (30 high-divergence TPS threads vs. 30 high-divergence GDS threads); evaluate_predictions() is a small precision/recall/F1 harness for testing the "helps developers find useful reports" claim against a labeled ground truth, however small.
• fetch.py: an optional thin wrapper around ReleaseTrain's /api/reddit endpoint, isolated from every other module so none of them need network access to run or be tested.
• cli.py: the releasetrain-sentiment console script (see "Command-line interface" above). Argument parsing and JSON I/O only, no logic of its own - every subcommand calls straight into one of the modules above.

Example: the keyword-contamination check

from releasetrain_sentiment import flags_technical_keyword

flags_technical_keyword("That redesign was a massive error")  # True
flags_technical_keyword("I am so tired of this company")       # False

The first example is the exact contamination Reviewer #7 describes: the keyword fires regardless of whether the comment is actually a bug report.

Example: training a classifier

from releasetrain_sentiment import train_classifier, classify, save_classifier

texts = ["crash on launch every time", "app freezes after the update",
         "I'm so tired of this company", "another pointless update, ugh"]
labels = ["TPS", "TPS", "GDS", "GDS"]

pipe = train_classifier(texts, labels)
classify(pipe, ["the app crashed again"])  # ["TPS"]
save_classifier(pipe, "tps_gds_model.joblib")

Known limitations

• No pretrained TPS/GDS classifier ships with this package. The original 542-post gold-labeled dataset was not available when it was built.
• baseline_vader.py is a best-effort reconstruction of a described flaw, not a verified reproduction of the original experiment code.
• TPSGDS542_CORPUS in corpus.py has unfilled fields (subreddit list, date range, labeling process) that need the real dataset to complete.

Releasing to PyPI

Publishing uses PyPI's Trusted Publishing: no API token is stored in this repo or anywhere else. One-time setup, done once by whoever owns the sberhe-se4cps PyPI account:

1. Go to https://pypi.org/manage/account/publishing/
2. Add a new pending publisher with:
   • PyPI project name: releasetrain-sentiment
   • Owner: SE4CPS
   • Repository name: releasetrain-sentiment
   • Workflow name: publish.yml
   • Environment name: pypi
3. Save it. This registers the project on PyPI without uploading anything yet.

After that, every GitHub Release published from this repo (tag it v0.1.0, draft a release from that tag) automatically builds and publishes to PyPI via .github/workflows/publish.yml. No further manual steps or secrets needed.

License

MIT