Hugging Face's logo

Datasets:
badashin
/
lince-sa-refined

Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
Spanish
English
Size:
< 1K
Tags:
code-switching
sentiment-analysis
spanish
english
spanglish
twitter
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
badashin commited on
Commit
8aee20b
·
verified ·
1 Parent(s): bf5497c

Initial release: 763 cultural-context label refinements (KSC 2025)

Browse files
Files changed (3) hide show
  1. README.md +134 -0
  2. build_refined_dataset.py +164 -0
  3. label_mapping.json +3058 -0
README.md ADDED
@@ -0,0 +1,134 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: cc-by-4.0
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+ task_categories:
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+ - text-classification
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+ language:
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+ - es
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+ - en
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+ tags:
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+ - code-switching
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+ - sentiment-analysis
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+ - spanish
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+ - english
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+ - spanglish
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+ - twitter
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+ - hispanic-american
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+ - low-resource
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+ pretty_name: LINCE SA Refined — Cultural-Context Relabeling for Spanish-English Code-Switching Sentiment Analysis
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+ size_categories:
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+ - 1K<n<10K
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+ source_datasets:
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+ - lince-benchmark/lince
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+ ---
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+
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+ # LINCE SA Refined — Cultural-Context Relabeling for Spanish-English Code-Switching Sentiment Analysis
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+
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+ This repository releases **763 sentiment label refinements** for the Spanish-English code-switching subset (`sa_spaeng`) of the [LINCE benchmark](https://huggingface.co/datasets/lince-benchmark/lince) (Aguilar et al., 2020). Refinements were produced by a trilingual annotator (Spanish / English / Korean) drawing on Hispanic-American social media conventions, and validated through controlled mBERT experiments.
27
+
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+ This work received an **Honorable Mention at the Korea Software Congress (KSC) 2025**.
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+
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+ ## What this is
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+
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+ A **reproducible label-mapping** layered on top of LINCE SA. The original LINCE data is *not* redistributed here — only the refinements and a script that applies them.
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+
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+ - `label_mapping.json` — 763 entries in `{sample_id: {original, corrected}}` format
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+ - `build_refined_dataset.py` — loads the original LINCE from Hugging Face and applies the refinements to reconstruct the refined set
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+
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+ ## What this is NOT
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+
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+ - A replacement for LINCE — the original benchmark remains the canonical source
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+ - A drop-in dataset — running the reproduction script is required to materialize the data
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+
42
+ ## How to use
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+
44
+ ```bash
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+ # 1. Install dependencies
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+ pip install datasets
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+
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+ # 2. Clone this repo
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+ git clone https://huggingface.co/datasets/badashin/lince-sa-refined
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+ cd lince-sa-refined
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+
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+ # 3. Run the reproduction script
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+ python build_refined_dataset.py
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+ # Output: refined_dataset.json (5,567 samples)
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+ ```
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+
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+ The script downloads LINCE SA `sa_spaeng` via `datasets.load_dataset`, filters for genuine Spanish-English code-switching (both languages with ≥ 2 tokens each), applies the 763 refinements where the recorded original label still matches, and drops samples whose `sample_id` appears in more than one split.
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+
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+ ## What's in the mapping
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+
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+ 763 sentiment label refinements with the following transition distribution:
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+
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+ | Original | → | Refined | Count | % |
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+ |----------|---|----------|------:|------:|
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+ | positive | → | neutral | 194 | 25.4% |
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+ | neutral | → | positive | 156 | 20.4% |
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+ | neutral | → | negative | 133 | 17.4% |
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+ | positive | → | negative | 114 | 14.9% |
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+ | negative | → | neutral | 89 | 11.7% |
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+ | negative | → | positive | 77 | 10.1% |
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+
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+ The dominant pattern is bidirectional positive ↔ neutral refinement (45.8%), reflecting cultural-pragmatic context — humor, formulaic greetings, sarcasm, and Hispanic-American social media conventions — where surface signals can mislead annotators less familiar with these registers.
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+
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+ ## Methodology (brief)
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+
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+ - Sample-level review of LINCE SA `sa_spaeng` train + validation splits
77
+ - Selection criteria: genuine Spanish-English code-switching (≥ 2 tokens in each language)
78
+ - Annotation by a trilingual researcher (ES / EN / KO) using Hispanic-American social media norms
79
+ - Conservative labeling for ambiguous cases (preferring `neutral` over false positives)
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+ - Final refined dataset: 5,567 code-switched samples after deduplication
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+
82
+ Full methodology, illustrative examples, and downstream results are in the paper.
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+
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+ ## Downstream impact
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+
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+ Controlled mBERT experiments using identical hyperparameters across original and refined data show **+4.0pp improvement** (56.6% → 60.6%) attributable to label refinement alone. A subsequent mBERT–XLM-R Late Fusion ensemble on the refined data reaches **67.15%**.
87
+
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+ ## Refined label distribution
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+
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+ After deduplication, the 5,567-sample refined set has the following label distribution:
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+
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+ | Label | Count | Share |
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+ |----------|------:|------:|
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+ | positive | 3,021 | 54.3% |
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+ | neutral | 1,519 | 27.3% |
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+ | negative | 1,027 | 18.4% |
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+
98
+ ## Citation
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+
100
+ If you use this dataset, please cite both the original LINCE benchmark and this work:
101
+
102
+ ```bibtex
103
+ @inproceedings{aguilar2020lince,
104
+ title = {LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation},
105
+ author = {Aguilar, Gustavo and Kar, Sudipta and Solorio, Thamar},
106
+ booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},
107
+ year = {2020}
108
+ }
109
+
110
+ @inproceedings{shin2025relabeling,
111
+ title = {Re-labeling Approach for Spanish-English Code-switching Sentiment Analysis: Impact of Data Quality Improvement},
112
+ author = {Shin, Bada and Kim, Sunoh},
113
+ booktitle = {Korea Software Congress (KSC)},
114
+ year = {2025},
115
+ note = {Honorable Mention}
116
+ }
117
+ ```
118
+
119
+ ## Acknowledgments
120
+
121
+ This work builds on the **LINCE benchmark** (Aguilar et al., 2020), which provides invaluable infrastructure for code-switching research. The Spanish-English sentiment analysis subset (`sa_spaeng`) used here was originally curated by the LINCE team.
122
+
123
+ This research was supported by the SW-Centered University Project (2024-0-00035) of the Ministry of Science and ICT and the Institute of Information & Communications Technology Planning & Evaluation (IITP), Republic of Korea.
124
+
125
+ ## License
126
+
127
+ - Label mapping (`label_mapping.json`) and reproduction script (`build_refined_dataset.py`) — **CC-BY-4.0**
128
+ - The original LINCE data is **not** redistributed and remains subject to its own terms (see https://ritual.uh.edu/lince/)
129
+
130
+ ## Links
131
+
132
+ - 📄 Paper: https://github.com/vamosbada/project-puente/blob/main/docs/paper.pdf
133
+ - 💻 Full project (code, notebooks, experiments): https://github.com/vamosbada/project-puente
134
+ - 🏛️ Original LINCE benchmark: https://huggingface.co/datasets/lince-benchmark/lince
build_refined_dataset.py ADDED
@@ -0,0 +1,164 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ build_refined_dataset.py
3
+
4
+ Reproduces the Refined Dataset described in the paper:
5
+ "Re-labeling Approach for Spanish-English Code-switching Sentiment Analysis:
6
+ Impact of Data Quality Improvement" (KSC 2025, Honorable Mention)
7
+
8
+ This script loads the original LINCE SA (sa_spaeng) split from Hugging Face,
9
+ applies the human-verified label refinements in label_mapping.json, and writes
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+ the resulting refined dataset (5,567 samples) to refined_dataset.json.
11
+
12
+ Usage:
13
+ python build_refined_dataset.py
14
+
15
+ Output:
16
+ refined_dataset.json — the refined dataset, ready for downstream training
17
+ """
18
+
19
+ import json
20
+ import os
21
+ from collections import Counter
22
+
23
+ # The original LINCE SA dataset is loaded via Hugging Face datasets.
24
+ # Install with: pip install datasets
25
+ from datasets import load_dataset
26
+
27
+
28
+ # Splits used to build the refined dataset.
29
+ # The test split is excluded because LINCE SA does not provide gold
30
+ # sentiment labels for the test split.
31
+ SPLITS_TO_USE = ["train", "validation"]
32
+
33
+ # LINCE SA returns the sentiment label as a string from this set.
34
+ VALID_LABELS = {"positive", "neutral", "negative"}
35
+
36
+
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+ def load_label_mapping(path="label_mapping.json"):
38
+ """Load the human-verified label refinements."""
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+ mapping_path = os.path.join(
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+ os.path.dirname(os.path.abspath(__file__)), path
41
+ )
42
+ with open(mapping_path, "r", encoding="utf-8") as f:
43
+ raw = json.load(f)
44
+ return raw["label_mapping"]
45
+
46
+
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+ def is_code_switched(lid_labels, min_tokens_per_lang=2):
48
+ """
49
+ A sample is treated as genuine Spanish-English code-switching when both
50
+ lang1 (English) and lang2 (Spanish) have at least min_tokens_per_lang
51
+ tokens.
52
+ """
53
+ counts = Counter(lid_labels)
54
+ return (
55
+ counts.get("lang1", 0) >= min_tokens_per_lang
56
+ and counts.get("lang2", 0) >= min_tokens_per_lang
57
+ )
58
+
59
+
60
+ def build_refined_dataset():
61
+ """
62
+ 1. Load LINCE SA (sa_spaeng) from Hugging Face
63
+ 2. Filter for genuine code-switching samples (both languages, >=2 tokens each)
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+ 3. Assign global sample_ids that align with label_mapping.json
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+ 4. Apply human-verified label refinements
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+ 5. Drop samples whose sample_id appears in more than one split
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+ 6. Write refined_dataset.json
68
+ """
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+ print("Loading label refinements from label_mapping.json ...")
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+ label_mapping = load_label_mapping()
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+ print(f" {len(label_mapping)} refinements loaded\n")
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+
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+ print("Loading LINCE SA from Hugging Face ...")
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+ dataset = load_dataset(
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+ "lince-benchmark/lince", "sa_spaeng",
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+ trust_remote_code=True,
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+ )
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+
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+ # Enumerate samples across train + validation, assigning global indices.
80
+ # The global index becomes the sample_id (sample_<idx>).
81
+ # This ID scheme is what label_mapping.json keys reference.
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+ all_samples = []
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+ global_idx = 0
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+
85
+ for split in SPLITS_TO_USE:
86
+ split_data = dataset[split]
87
+ print(f" {split}: {len(split_data)} samples")
88
+
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+ for row in split_data:
90
+ sample_id = f"sample_{global_idx}"
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+ global_idx += 1
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+
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+ sentiment = row["sa"]
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+ if sentiment not in VALID_LABELS:
95
+ continue
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+ if not is_code_switched(row["lid"]):
97
+ continue
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+
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+ # Apply a refinement when one is recorded for this sample_id.
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+ original_sentiment = sentiment
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+ if sample_id in label_mapping:
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+ ref = label_mapping[sample_id]
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+ # Sanity guard: only apply when the source label still
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+ # matches the recorded original.
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+ if ref["original"] == sentiment:
106
+ sentiment = ref["corrected"]
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+
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+ all_samples.append({
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+ "id": sample_id,
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+ "text": " ".join(row["words"]),
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+ "tokens": row["words"],
112
+ "lid_labels": row["lid"],
113
+ "sentiment": sentiment,
114
+ "original_sentiment": original_sentiment,
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+ "label_refined": sentiment != original_sentiment,
116
+ })
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+
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+ print(f"\nCode-switched samples collected: {len(all_samples)}")
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+
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+ # Drop samples whose sample_id is shared across splits, to avoid
121
+ # ambiguous labels in the final set.
122
+ id_counts = Counter(s["id"] for s in all_samples)
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+ duplicate_ids = {sid for sid, c in id_counts.items() if c > 1}
124
+ print(f"Cross-split duplicate IDs removed: {len(duplicate_ids)}")
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+
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+ refined = [s for s in all_samples if s["id"] not in duplicate_ids]
127
+ print(f"Refined dataset size: {len(refined)}")
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+
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+ dist = Counter(s["sentiment"] for s in refined)
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+ print("\nLabel distribution:")
131
+ for label, count in sorted(dist.items()):
132
+ print(f" {label}: {count} ({count / len(refined) * 100:.1f}%)")
133
+
134
+ refinements_applied = sum(1 for s in refined if s["label_refined"])
135
+ print(f"\nLabel refinements applied: {refinements_applied}")
136
+
137
+ output_path = os.path.join(
138
+ os.path.dirname(os.path.abspath(__file__)),
139
+ "refined_dataset.json",
140
+ )
141
+ output = {
142
+ "metadata": {
143
+ "dataset": "LINCE SA Refined (Spanish-English Sentiment)",
144
+ "source": "lince-benchmark/lince [sa_spaeng] via Hugging Face",
145
+ "paper": (
146
+ "Re-labeling Approach for Spanish-English Code-switching "
147
+ "Sentiment Analysis (KSC 2025)"
148
+ ),
149
+ "total_samples": len(refined),
150
+ "label_refinements_applied": refinements_applied,
151
+ "label_distribution": dict(dist),
152
+ },
153
+ "data": refined,
154
+ }
155
+
156
+ with open(output_path, "w", encoding="utf-8") as f:
157
+ json.dump(output, f, ensure_ascii=False, indent=2)
158
+
159
+ print(f"\nSaved to {output_path}")
160
+ print("Done.")
161
+
162
+
163
+ if __name__ == "__main__":
164
+ build_refined_dataset()
label_mapping.json ADDED
@@ -0,0 +1,3058 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_readme": "Label corrections applied to the original LINCE SA dataset. Contains only the 763 samples where the label was changed during human relabeling. sample_id corresponds to the row index in the combined LINCE SA dataset (train + validation splits). See build_refined_dataset.py for reproduction instructions.",
3
+ "total_corrections": 763,
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+ "label_mapping": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "original": "neutral",
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+ "corrected": "negative"
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+ },
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+ "sample_43": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_58": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_77": {
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+ "original": "negative",
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+ "corrected": "positive"
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+ },
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+ "sample_78": {
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+ "original": "negative",
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+ "corrected": "positive"
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+ },
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+ "sample_87": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_91": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_96": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_101": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_102": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_121": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_134": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_135": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_182": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_183": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_187": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_212": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_223": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_241": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_248": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_275": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_295": {
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+ "original": "positive",
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+ "corrected": "negative"
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+ },
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+ "sample_308": {
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+ "original": "neutral",
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+ "corrected": "negative"
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+ },
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+ "sample_326": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_328": {
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+ "original": "negative",
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+ "corrected": "positive"
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+ },
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+ "sample_338": {
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+ "original": "negative",
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+ "corrected": "positive"
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+ },
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+ "sample_350": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_353": {
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+ "original": "neutral",
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+ "corrected": "negative"
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+ },
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+ "sample_356": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_357": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_358": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_375": {
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+ "original": "negative",
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+ "corrected": "positive"
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+ },
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+ "sample_442": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_450": {
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_483": {
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+ "original": "positive",
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+ "corrected": "negative"
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+ },
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+ "sample_491": {
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_507": {
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+ "original": "negative",
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+ "corrected": "positive"
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+ },
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+ "sample_508": {
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_525": {
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+ "original": "neutral",
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+ "corrected": "negative"
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+ },
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+ "sample_528": {
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+ "original": "positive",
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+ "corrected": "negative"
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+ },
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+ "sample_530": {
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_538": {
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_557": {
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_567": {
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+ "original": "neutral",
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+ "corrected": "negative"
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+ },
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+ "sample_605": {
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_607": {
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "original": "positive",
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+ "corrected": "neutral"
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+ "original": "positive",
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+ "corrected": "neutral"
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+ "original": "neutral",
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+ "corrected": "negative"
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+ },
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+ "original": "neutral",
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+ "corrected": "negative"
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+ },
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+ "original": "neutral",
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+ "corrected": "positive"
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+ "original": "neutral",
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+ "corrected": "negative"
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+ "original": "positive",
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+ "corrected": "neutral"
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+ "sample_770": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_803": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "sample_811": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_830": {
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_844": {
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_872": {
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+ "original": "neutral",
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+ "corrected": "negative"
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+ },
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+ "sample_875": {
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_897": {
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_900": {
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+ "original": "positive",
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+ "corrected": "negative"
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+ },
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+ "original": "neutral",
283
+ "corrected": "negative"
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+ },
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "original": "neutral",
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+ "corrected": "negative"
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "original": "neutral",
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+ "corrected": "negative"
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+ "original": "positive",
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+ "corrected": "neutral"
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+ },
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+ "sample_982": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "original": "neutral",
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+ "corrected": "negative"
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+ },
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+ "sample_1028": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ },
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+ "original": "neutral",
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+ "corrected": "positive"
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+ "sample_1062": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ "sample_1067": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ "original": "neutral",
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+ "corrected": "positive"
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+ "original": "neutral",
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+ "corrected": "positive"
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+ "original": "neutral",
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+ "corrected": "negative"
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+ "original": "neutral",
343
+ "corrected": "positive"
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+ "original": "neutral",
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+ "corrected": "positive"
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+ "original": "neutral",
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+ "corrected": "negative"
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+ "original": "neutral",
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+ "corrected": "negative"
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+ "sample_1154": {
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+ "original": "positive",
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+ "original": "negative",
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+ "corrected": "neutral"
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+ },
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+ "sample_1299": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ "original": "negative",
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+ "corrected": "neutral"
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+ "original": "negative",
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+ "corrected": "neutral"
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+ "original": "positive",
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+ "corrected": "negative"
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+ "original": "positive",
383
+ "corrected": "neutral"
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+ },
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+ "sample_1624": {
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+ "original": "positive",
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+ "corrected": "negative"
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+ },
389
+ "sample_1629": {
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+ "original": "positive",
391
+ "corrected": "neutral"
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+ "sample_1723": {
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+ "original": "positive",
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+ "corrected": "negative"
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+ "sample_1763": {
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+ "original": "positive",
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+ "corrected": "negative"
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+ "sample_1809": {
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+ "original": "neutral",
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+ "corrected": "positive"
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+ "sample_1821": {
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+ "original": "neutral",
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+ "corrected": "negative"
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+ "sample_1827": {
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+ "corrected": "negative"
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+ "original": "neutral",
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+ "corrected": "positive"
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+ "original": "positive",
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+ "original": "positive",
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+ "corrected": "negative"
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+ "original": "positive",
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+ "corrected": "negative"
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+ "original": "neutral",
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+ "corrected": "positive"
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+ "original": "positive",
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+ "corrected": "negative"
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+ "corrected": "positive"
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+ "original": "positive",
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+ "original": "positive",
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+ "corrected": "neutral"
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+ "original": "positive",
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+ "corrected": "negative"
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