leksara 0.2.1

Library pemrosesan teks Bahasa Indonesia untuk domain e-commerce (cleaning, PII masking, review mining, pipeline).

LEKSARA

Transforming Text, Empowering Insights Instantly

Indonesian-language text preparation toolkit for production review pipelines: clean, mask, and normalize in a single pass.

The library ships linguistic resources, preset-driven orchestration, and modular helpers so data teams can audit raw text, remediate sensitive content, and standardize noisy reviews without rebuilding the pipeline from scratch.

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Table of Contents

• Why Leksara
• Quickstart
• How Leksara Fits Together
• Documentation Map
• Contributing & Support

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Why Leksara

Capability	What you get	Key modules
CartBoard review intake	Dataset audits with PII flags, rating detection, and noise diagnostics ready for dashboards.	leksara.frames.cartboard
Composable cleaning utilities	Reusable HTML stripping, casing, stopword, emoji, punctuation, and numeric cleanup helpers.	leksara.function
PII masking & redaction	Regex-backed replacement for Indonesian phones, emails, addresses, and national IDs with configurable modes.	leksara.pattern plus leksara/resources/regex_patterns/
Review-focused normalization	Slang/acronym expansion, contraction repair, rating extraction, and elongation trimming for Bahasa Indonesia.	leksara.functions.review
ReviewChain orchestrator	leksara(...) wrapper and ReviewChain class for preset pipelines, benchmarking, and hybrid custom flows.	leksara.core.chain
Resource-driven customization	Drop in your own dictionaries and regex rules to adapt cleaners for new verticals.	leksara/resources/

Deep dives live in docs/features.md alongside API tables, dependencies, and ready-to-run notebooks.

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Quickstart

1. Create and activate a virtual environment

   python -m venv .venv
   .\.venv\Scripts\Activate.ps1
   pip install leksara
   

   Optional extras and troubleshooting tips are documented in docs/installation.md.

2. Clean a review column with the ecommerce preset

   import pandas as pd
   from leksara import leksara
   
   df = pd.DataFrame(
       {
           "review_id": [101, 102],
           "review_text": [
               "<p>Barangnya mantul!!! Email saya [e-mail], WA 0812-3456-7890</p>",
               "Kualitasnya ⭐⭐⭐⭐, pengiriman 4/5. Hubungi [phone]",
           ],
       }
   )
   
   df["clean_text"] = leksara(df["review_text"], preset="ecommerce_review")
   print(df[["review_id", "clean_text"]])
   

   review_id	clean_text
   101	barang mantap email [EMAIL] wa [PHONE_NUMBER]
   102	kualitas 4.0 kirim 4.0 hubung [PHONE_NUMBER]

3. Audit raw text with CartBoard

   from leksara.frames.cartboard import get_flags, get_stats
   
   flags = get_flags(df, text_column="review_text")
   stats = get_stats(df, text_column="review_text")
   
   print(flags[["review_id", "pii_flag", "rating_flag", "non_alphabetical_flag"]])
   print(stats.iloc[0]["stats"])  # nested histogram of noise sources
   

4. Compose a tailored pipeline with ReviewChain

   from leksara import ReviewChain
   from leksara.function import case_normal, remove_punctuation, remove_stopwords
   from leksara.pattern import replace_email, replace_phone
   
   chain = ReviewChain.from_steps(
       patterns=[
           (replace_phone, {"mode": "replace"}),
           (replace_email, {"mode": "replace"}),
       ],
       functions=[case_normal, remove_stopwords, remove_punctuation],
   )
   
   cleaned, metrics = chain.transform(df["review_text"], benchmark=True)
   

   metrics includes per-step timings so you can spot bottlenecks or confirm PII masks run before downstream cleaners.

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How Leksara Fits Together

Layer	Purpose	Entry points
Pipelines	High-level orchestration that accepts raw sequences and returns cleaned text plus optional benchmarks.	leksara(...), ReviewChain
Frames	Review-table utilities for bulk audits and dashboard-friendly stats.	leksara.frames.cartboard
Functions	Composable cleaning helpers mirrored from the implementation modules.	leksara.function
Patterns	Opt-in masking utilities with regex rules for PII.	leksara.pattern
Resources	Dictionaries, regex rules, and whitelists that drive domain knowledge.	leksara/resources/
Logging & Benchmarking	Optional hooks for throughput tuning and step-level visibility.	leksara.core.logging, benchmark=True

Architectural notes, data contracts, and extension points for each layer are captured in docs/features.md.

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Documentation Map

Topic	When to read	Location
Installation & environment	Provisioning a workstation or CI agent	docs/installation.md
Feature deep dives	Behavioral details, configuration knobs, and dependencies	docs/features.md
Public API reference	Signatures, argument descriptions, return payloads	docs/api.md
Worked examples	Copy/paste recipes for notebooks or pipelines	docs/examples.md
Dependency matrix	Optional packages and enterprise alignment	docs/dependencies.md
Contributing guide	Environment, style, testing, documentation expectations	docs/contributing.md

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Contributing & Support

• Read docs/contributing.md before opening a pull request; it outlines environment setup, code style, testing, and documentation requirements.
• File issues on GitHub with reproducible examples, presets used, optional dependencies, and OS details when reporting pipeline differences.
• Commercial or large-scale users should wrap ReviewChain with automated smoke tests to detect upstream dictionary or regex changes early.

Leksara is licensed under the terms specified in LICENSE.