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remove sentiment from docs

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thomashacker 2022-10-31 14:33:48 +01:00
parent 83f518b412
commit b00d4149a7
6 changed files with 8 additions and 10 deletions
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2
website/docs/api/doc.md
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@ -761,7 +761,6 @@ The L2 norm of the document's vector representation.
| `user_data` | A generic storage area, for user custom data. ~~Dict[str, Any]~~ | | `user_data` | A generic storage area, for user custom data. ~~Dict[str, Any]~~ |
| `lang` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~int~~ | | `lang` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~int~~ |
| `lang_` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~str~~ | | `lang_` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~str~~ |
| `sentiment` | The document's positivity/negativity score, if available. ~~float~~ |
| `user_hooks` | A dictionary that allows customization of the `Doc`'s properties. ~~Dict[str, Callable]~~ | | `user_hooks` | A dictionary that allows customization of the `Doc`'s properties. ~~Dict[str, Callable]~~ |
| `user_token_hooks` | A dictionary that allows customization of properties of `Token` children. ~~Dict[str, Callable]~~ | | `user_token_hooks` | A dictionary that allows customization of properties of `Token` children. ~~Dict[str, Callable]~~ |
| `user_span_hooks` | A dictionary that allows customization of properties of `Span` children. ~~Dict[str, Callable]~~ | | `user_span_hooks` | A dictionary that allows customization of properties of `Span` children. ~~Dict[str, Callable]~~ |
@ -785,7 +784,6 @@ serialization by passing in the string names via the `exclude` argument.
| Name | Description | | Name | Description |
| ------------------ | --------------------------------------------- | | ------------------ | --------------------------------------------- |
| `text` | The value of the `Doc.text` attribute. | | `text` | The value of the `Doc.text` attribute. |
| `sentiment` | The value of the `Doc.sentiment` attribute. |
| `tensor` | The value of the `Doc.tensor` attribute. | | `tensor` | The value of the `Doc.tensor` attribute. |
| `user_data` | The value of the `Doc.user_data` dictionary. | | `user_data` | The value of the `Doc.user_data` dictionary. |
| `user_data_keys` | The keys of the `Doc.user_data` dictionary. | | `user_data_keys` | The keys of the `Doc.user_data` dictionary. |

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website/docs/api/lexeme.md
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@ -161,4 +161,3 @@ The L2 norm of the lexeme's vector representation.
| `lang_` | Language of the parent vocabulary. ~~str~~ | | `lang_` | Language of the parent vocabulary. ~~str~~ |
| `prob` | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). ~~float~~ | | `prob` | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). ~~float~~ |
| `cluster` | Brown cluster ID. ~~int~~ | | `cluster` | Brown cluster ID. ~~int~~ |
| `sentiment` | A scalar value indicating the positivity or negativity of the lexeme. ~~float~~ |

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website/docs/api/span.md
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@ -565,5 +565,4 @@ overlaps with will be returned.
| `ent_id_` | Alias for `id_`: the span's ID. ~~str~~ | | `ent_id_` | Alias for `id_`: the span's ID. ~~str~~ |
| `id` | The hash value of the span's ID. ~~int~~ | | `id` | The hash value of the span's ID. ~~int~~ |
| `id_` | The span's ID. ~~str~~ | | `id_` | The span's ID. ~~str~~ |
| `sentiment` | A scalar value indicating the positivity or negativity of the span. ~~float~~ |
| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ | | `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ |

1
website/docs/api/token.md
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@ -470,7 +470,6 @@ The L2 norm of the token's vector representation.
| `lang_` | Language of the parent document's vocabulary. ~~str~~ | | `lang_` | Language of the parent document's vocabulary. ~~str~~ |
| `prob` | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). ~~float~~ | | `prob` | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). ~~float~~ |
| `idx` | The character offset of the token within the parent document. ~~int~~ | | `idx` | The character offset of the token within the parent document. ~~int~~ |
| `sentiment` | A scalar value indicating the positivity or negativity of the token. ~~float~~ |
| `lex_id` | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ | | `lex_id` | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ |
| `rank` | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ | | `rank` | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ |
| `cluster` | Brown cluster ID. ~~int~~ | | `cluster` | Brown cluster ID. ~~int~~ |

2
website/docs/usage/processing-pipelines.md
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@ -1400,7 +1400,7 @@ separation and makes it easier to ensure backwards compatibility. For example,
if you've implemented your own `.coref` property and spaCy claims it one day, if you've implemented your own `.coref` property and spaCy claims it one day,
it'll break your code. Similarly, just by looking at the code, you'll it'll break your code. Similarly, just by looking at the code, you'll
immediately know what's built-in and what's custom – for example, immediately know what's built-in and what's custom – for example,
`doc.sentiment` is spaCy, while `doc._.sent_score` isn't. `doc.lang` is spaCy, while `doc._.language` isn't.
</Accordion> </Accordion>

11
website/docs/usage/rule-based-matching.md
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@ -777,6 +777,8 @@ whitespace, making them easy to match as well.
from spacy.lang.en import English from spacy.lang.en import English
from spacy.matcher import Matcher from spacy.matcher import Matcher
Doc.set_extension("sentiment", default=0.0)
nlp = English() # We only want the tokenizer, so no need to load a pipeline nlp = English() # We only want the tokenizer, so no need to load a pipeline
matcher = Matcher(nlp.vocab) matcher = Matcher(nlp.vocab)
@ -791,9 +793,9 @@ neg_patterns = [[{"ORTH": emoji}] for emoji in neg_emoji]
def label_sentiment(matcher, doc, i, matches): def label_sentiment(matcher, doc, i, matches):
match_id, start, end = matches[i] match_id, start, end = matches[i]
if doc.vocab.strings[match_id] == "HAPPY": # Don't forget to get string! if doc.vocab.strings[match_id] == "HAPPY": # Don't forget to get string!
doc.sentiment += 0.1 # Add 0.1 for positive sentiment doc._.sentiment += 0.1 # Add 0.1 for positive sentiment
elif doc.vocab.strings[match_id] == "SAD": elif doc.vocab.strings[match_id] == "SAD":
doc.sentiment -= 0.1 # Subtract 0.1 for negative sentiment doc._.sentiment -= 0.1 # Subtract 0.1 for negative sentiment
matcher.add("HAPPY", pos_patterns, on_match=label_sentiment) # Add positive pattern matcher.add("HAPPY", pos_patterns, on_match=label_sentiment) # Add positive pattern
matcher.add("SAD", neg_patterns, on_match=label_sentiment) # Add negative pattern matcher.add("SAD", neg_patterns, on_match=label_sentiment) # Add negative pattern
@ -826,13 +828,14 @@ from emojipedia import Emojipedia # Installation: pip install emojipedia
from spacy.tokens import Span # Get the global Span object from spacy.tokens import Span # Get the global Span object
Span.set_extension("emoji_desc", default=None) # Register the custom attribute Span.set_extension("emoji_desc", default=None) # Register the custom attribute
Doc.set_extension("sentiment", default=0.0)
def label_sentiment(matcher, doc, i, matches): def label_sentiment(matcher, doc, i, matches):
match_id, start, end = matches[i] match_id, start, end = matches[i]
if doc.vocab.strings[match_id] == "HAPPY": # Don't forget to get string! if doc.vocab.strings[match_id] == "HAPPY": # Don't forget to get string!
doc.sentiment += 0.1 # Add 0.1 for positive sentiment doc._.sentiment += 0.1 # Add 0.1 for positive sentiment
elif doc.vocab.strings[match_id] == "SAD": elif doc.vocab.strings[match_id] == "SAD":
doc.sentiment -= 0.1 # Subtract 0.1 for negative sentiment doc._.sentiment -= 0.1 # Subtract 0.1 for negative sentiment
span = doc[start:end] span = doc[start:end]
emoji = Emojipedia.search(span[0].text) # Get data for emoji emoji = Emojipedia.search(span[0].text) # Get data for emoji
span._.emoji_desc = emoji.title # Assign emoji description span._.emoji_desc = emoji.title # Assign emoji description