File size: 9,696 Bytes
a092d54
 
 
e1645d7
 
 
228859a
a092d54
e1645d7
a092d54
2c1f9dd
 
a092d54
 
 
 
 
 
 
 
e1645d7
 
2c1f9dd
 
a092d54
 
2c1f9dd
e1645d7
 
2c1f9dd
 
 
 
 
 
228859a
2c1f9dd
 
 
 
 
 
228859a
2c1f9dd
 
e1645d7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
447f97e
e1645d7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
447f97e
2c1f9dd
 
 
 
 
 
 
228859a
2c1f9dd
 
e1645d7
2c1f9dd
228859a
 
e1645d7
 
 
447f97e
 
2c1f9dd
 
 
 
228859a
2c1f9dd
 
447f97e
2c1f9dd
228859a
2c1f9dd
 
 
 
 
 
 
 
 
 
 
 
e1645d7
 
 
 
 
 
 
 
 
 
228859a
 
 
 
447f97e
228859a
e1645d7
228859a
e1645d7
228859a
 
 
a092d54
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d09d1f6
a092d54
 
 
 
 
 
 
 
 
d09d1f6
a092d54
d09d1f6
a092d54
 
 
 
 
d09d1f6
 
 
a092d54
 
d09d1f6
0ca5366
a092d54
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2c1f9dd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a092d54
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d09d1f6
a092d54
 
2c1f9dd
 
a092d54
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
from __future__ import annotations

import bz2
import json
import re
from functools import lru_cache
from typing import TYPE_CHECKING, Literal, Sequence

import emoji
import pandas as pd
import spacy
from tqdm import tqdm

from app.constants import (
    AMAZONREVIEWS_PATH,
    AMAZONREVIEWS_URL,
    IMDB50K_PATH,
    IMDB50K_URL,
    SENTIMENT140_PATH,
    SENTIMENT140_URL,
    SLANGMAP_PATH,
    SLANGMAP_URL,
    TEST_DATASET_PATH,
    TEST_DATASET_URL,
)

if TYPE_CHECKING:
    from re import Pattern

    from spacy.tokens import Doc

__all__ = ["load_data", "tokenize"]


try:
    nlp = spacy.load("en_core_web_sm")
except OSError:
    print("Downloading spaCy model...")

    from spacy.cli import download as spacy_download

    spacy_download("en_core_web_sm")
    nlp = spacy.load("en_core_web_sm")


@lru_cache(maxsize=1)
def slang() -> tuple[Pattern, dict[str, str]]:
    """Compile a re pattern for slang terms.

    Returns:
        Slang pattern and mapping

    Raises:
        FileNotFoundError: If the file is not found
    """
    if not SLANGMAP_PATH.exists():
        # msg = f"Missing slang mapping file: {SLANG_PATH}"
        msg = (
            f"Slang mapping file not found at: '{SLANGMAP_PATH}'\n"
            "Please download the file from:\n"
            f"{SLANGMAP_URL}"
        )  # fmt: off
        raise FileNotFoundError(msg)

    with SLANGMAP_PATH.open() as f:
        mapping = json.load(f)

    return re.compile(r"\b(" + "|".join(map(re.escape, mapping.keys())) + r")\b"), mapping


def _clean(text: str) -> str:
    """Perform basic text cleaning.

    Args:
        text: Text to clean

    Returns:
        Cleaned text
    """
    # Make text lowercase
    text = text.lower()

    # Remove HTML tags
    text = re.sub(r"<[^>]*>", "", text)

    # Map slang terms
    slang_pattern, slang_mapping = slang()
    text = slang_pattern.sub(lambda x: slang_mapping[x.group()], text)

    # Remove acronyms and abbreviations
    # text = re.sub(r"(?:[a-z]\.){2,}", "", text)
    text = re.sub(r"\b(?:[a-z]\.?)(?:[a-z]\.)\b", "", text)

    # Remove honorifics
    text = re.sub(r"\b(?:mr|mrs|ms|dr|prof|sr|jr)\.?\b", "", text)

    # Remove year abbreviations
    text = re.sub(r"\b(?:\d{3}0|\d0)s?\b", "", text)

    # Remove hashtags
    text = re.sub(r"#[^\s]+", "", text)

    # Replace mentions with a generic tag
    text = re.sub(r"@[^\s]+", "user", text)

    # Replace X/Y with X or Y
    text = re.sub(r"\b([a-z]+)[//]([a-z]+)\b", r"\1 or \2", text)

    # Convert emojis to text
    text = emoji.demojize(text, delimiters=("emoji_", ""))

    # Remove special characters
    text = re.sub(r"[^a-z0-9\s]", "", text)

    # EXTRA: imdb50k specific cleaning
    text = re.sub(r"mst3k", "", text)  # Very common acronym for Mystery Science Theater 3000

    return text.strip()


def _lemmatize(doc: Doc, threshold: int = 3) -> Sequence[str]:
    """Lemmatize the provided text using spaCy.

    Args:
        doc: spaCy document
        threshold: Minimum character length of tokens

    Returns:
        Sequence of lemmatized tokens
    """
    return [
        tok
        for token in doc
        if not token.is_stop  # Ignore stop words
        and not token.is_punct  # Ignore punctuation
        and not token.like_email  # Ignore email addresses
        and not token.like_url  # Ignore URLs
        and not token.like_num  # Ignore numbers
        and token.is_alpha  # Ignore non-alphabetic tokens
        and (len(tok := token.lemma_.lower().strip()) >= threshold)  # Ignore short tokens
    ]


def tokenize(
    text_data: Sequence[str],
    batch_size: int = 512,
    n_jobs: int = 4,
    character_threshold: int = 3,
    show_progress: bool = True,
) -> Sequence[Sequence[str]]:
    """Tokenize the provided text using spaCy.

    Args:
        text_data: Text data to tokenize
        batch_size: Batch size for tokenization
        n_jobs: Number of parallel jobs
        character_threshold: Minimum character length of tokens
        show_progress: Whether to show a progress bar

    Returns:
        Tokenized text data
    """
    text_data = [
        _clean(text)
        for text in tqdm(
            text_data,
            desc="Cleaning",
            unit="doc",
            disable=not show_progress,
        )
    ]

    return pd.Series(
        [
            _lemmatize(doc, character_threshold)
            for doc in tqdm(
                nlp.pipe(text_data, batch_size=batch_size, n_process=n_jobs, disable=["parser", "ner"]),
                total=len(text_data),
                desc="Lemmatization",
                unit="doc",
                disable=not show_progress,
            )
        ],
    )


def load_sentiment140(include_neutral: bool = False) -> tuple[list[str], list[int]]:
    """Load the sentiment140 dataset and make it suitable for use.

    Args:
        include_neutral: Whether to include neutral sentiment

    Returns:
        Text and label data

    Raises:
        FileNotFoundError: If the dataset is not found
    """
    # Check if the dataset exists
    if not SENTIMENT140_PATH.exists():
        msg = (
            f"Sentiment140 dataset not found at: '{SENTIMENT140_PATH}'\n"
            "Please download the dataset from:\n"
            f"{SENTIMENT140_URL}"
        )
        raise FileNotFoundError(msg)

    # Load the dataset
    data = pd.read_csv(
        SENTIMENT140_PATH,
        encoding="ISO-8859-1",
        names=[
            "target",  # 0 = negative, 2 = neutral, 4 = positive
            "id",  # The id of the tweet
            "date",  # The date of the tweet
            "flag",  # The query, NO_QUERY if not present
            "user",  # The user that tweeted
            "text",  # The text of the tweet
        ],
    )

    # Ignore rows with neutral sentiment
    if not include_neutral:
        data = data[data["target"] != 2]

    # Map sentiment values
    data["sentiment"] = data["target"].map(
        {
            0: 0,  # Negative
            4: 1,  # Positive
            2: 2,  # Neutral
        },
    )

    # Return as lists
    return data["text"].tolist(), data["sentiment"].tolist()


def load_amazonreviews() -> tuple[list[str], list[int]]:
    """Load the amazonreviews dataset and make it suitable for use.

    Returns:
        Text and label data

    Raises:
        FileNotFoundError: If the dataset is not found
    """
    # Check if the dataset exists
    if not AMAZONREVIEWS_PATH.exists():
        msg = (
            f"Amazonreviews dataset not found at: '{AMAZONREVIEWS_PATH}'\n"
            "Please download the dataset from:\n"
            f"{AMAZONREVIEWS_URL}"
        )
        raise FileNotFoundError(msg)

    # Load the dataset
    with bz2.BZ2File(AMAZONREVIEWS_PATH) as f:
        dataset = [line.decode("utf-8") for line in f]

    # Split the data into labels and text
    labels, texts = zip(*(line.split(" ", 1) for line in dataset))

    # Map sentiment values
    sentiments = [int(label.split("__label__")[1]) - 1 for label in labels]

    # Return as lists
    return texts, sentiments


def load_imdb50k() -> tuple[list[str], list[int]]:
    """Load the imdb50k dataset and make it suitable for use.

    Returns:
        Text and label data

    Raises:
        FileNotFoundError: If the dataset is not found
    """
    # Check if the dataset exists
    if not IMDB50K_PATH.exists():
        msg = (
            f"IMDB50K dataset not found at: '{IMDB50K_PATH}'\n"
            "Please download the dataset from:\n"
            f"{IMDB50K_URL}"
        )  # fmt: off
        raise FileNotFoundError(msg)

    # Load the dataset
    data = pd.read_csv(IMDB50K_PATH)

    # Map sentiment values
    data["sentiment"] = data["sentiment"].map(
        {
            "positive": 1,
            "negative": 0,
        },
    )

    # Return as lists
    return data["review"].tolist(), data["sentiment"].tolist()


def load_test(include_neutral: bool = False) -> tuple[list[str], list[int]]:
    """Load the test dataset and make it suitable for use.

    Args:
        include_neutral: Whether to include neutral sentiment

    Returns:
        Text and label data

    Raises:
        FileNotFoundError: If the dataset is not found
    """
    # Check if the dataset exists
    if not TEST_DATASET_PATH.exists():
        msg = (
            f"Test dataset not found at: '{TEST_DATASET_PATH}'\n"
            "Please download the dataset from:\n"
            f"{TEST_DATASET_URL}"
        )
        raise FileNotFoundError(msg)

    # Load the dataset
    data = pd.read_csv(TEST_DATASET_PATH)

    # Ignore rows with neutral sentiment
    if not include_neutral:
        data = data[data["label"] != 1]

    # Map sentiment values
    data["label"] = data["label"].map(
        {
            0: 0,  # Negative
            1: 1,  # Neutral
            2: 2,  # Positive
        },
    )

    # Return as lists
    return data["text"].tolist(), data["label"].tolist()


def load_data(dataset: Literal["sentiment140", "amazonreviews", "imdb50k", "test"]) -> tuple[list[str], list[int]]:
    """Load and preprocess the specified dataset.

    Args:
        dataset: Dataset to load

    Returns:
        Text and label data

    Raises:
        ValueError: If the dataset is not recognized
    """
    match dataset:
        case "sentiment140":
            return load_sentiment140(include_neutral=False)
        case "amazonreviews":
            return load_amazonreviews()
        case "imdb50k":
            return load_imdb50k()
        case "test":
            return load_test(include_neutral=False)
        case _:
            msg = f"Unknown dataset: {dataset}"
            raise ValueError(msg)