v1.1: inclusion criteria, leakage-aware splits, rebuild script for unlicensed sources
3fcd003 verified | """Readers for the four public corpora that state no licence and are therefore not | |
| redistributed: each returns the raw item texts from the authors' own release, so | |
| that release/rebuild.py can reconstruct our splits from a text-hash manifest. | |
| Every reader takes the directory into which the authors' GitHub repository was | |
| unpacked and returns a list of item texts. Labels are not taken from the source: | |
| the manifest carries our labels, so the rebuild is identical to the benchmark | |
| even where our label differs from the source's (e.g. ISE-Hate, 98.7% agreement). | |
| """ | |
| import glob | |
| import io | |
| import os | |
| import zipfile | |
| import unicodedata | |
| import pandas as pd | |
| REPOS = { | |
| "usc": "MuhammadYaseenKhan/Urdu-Sentiment-Corpus", | |
| "ise": "hammad7007/ISE_dataset", | |
| "btt": "MaazAmjad/Datasets-for-Urdu-news", | |
| "axg": "HjH-Whu-CRC/Ax-to-Grind-Urdu", | |
| } | |
| # benchmark domain -> source reader | |
| DOMAIN_SOURCE = { | |
| "Nastaliq/Sentiment Analysis/Domain_A_Twitter_Social_Media": "usc", | |
| "Nastaliq/Hate Speech/Domain_A_Social_Media_Offensive": "ise", | |
| "Nastaliq/Fake News Detection/Domain_A_Multi_Topic_News": "btt", | |
| "Nastaliq/Fake News Detection/Domain_B_Ax_to_Grind": "axg", | |
| } | |
| def norm(text): | |
| """Whitespace-collapsed text without byte-order marks or zero-width characters.""" | |
| t = unicodedata.normalize("NFKC", str(text)) | |
| for ch in ("", "", "", "", "", ""): | |
| t = t.replace(ch, "") | |
| return " ".join(t.split()) | |
| def _read_csv_any(f): | |
| for enc in ("utf-8-sig", "cp1256", "utf-16", "cp1252"): | |
| try: | |
| return pd.read_csv(f, encoding=enc) | |
| except (UnicodeError, pd.errors.ParserError): | |
| continue | |
| return pd.DataFrame() | |
| def _one(root, pattern): | |
| hits = glob.glob(os.path.join(root, "**", pattern), recursive=True) | |
| if not hits: | |
| raise FileNotFoundError(f"{pattern} not found under {root}") | |
| return hits[0] | |
| def read_usc(root): | |
| return pd.read_csv(_one(root, "urdu-sentiment-corpus-v1.tsv"), sep="\t").Tweet.astype(str).tolist() | |
| def read_ise(root): | |
| out = [] | |
| for f in glob.glob(os.path.join(root, "**", "*.xlsx"), recursive=True): | |
| df = pd.read_excel(f) | |
| col = next((c for c in df.columns if "text" in str(c).lower() or "tweet" == str(c).lower()), None) | |
| if col is not None: | |
| out += df[col].astype(str).tolist() | |
| return out | |
| def read_btt(root): | |
| out = [] | |
| for z in glob.glob(os.path.join(root, "**", "*.zip"), recursive=True): | |
| with zipfile.ZipFile(z) as zf: | |
| for name in zf.namelist(): | |
| if name.endswith(".txt"): | |
| raw = zf.read(name) | |
| for enc in ("utf-8-sig", "utf-16", "cp1256"): | |
| try: | |
| out.append(raw.decode(enc)) | |
| break | |
| except UnicodeDecodeError: | |
| continue | |
| return out | |
| def read_axg(root): | |
| out = [] | |
| for f in glob.glob(os.path.join(root, "**", "*.csv"), recursive=True): | |
| df = _read_csv_any(f) | |
| col = next((c for c in df.columns if "news" in str(c).lower()), None) | |
| if col is not None: | |
| out += df[col].astype(str).tolist() | |
| return out | |
| READERS = {"usc": read_usc, "ise": read_ise, "btt": read_btt, "axg": read_axg} | |