remove sentiment attribute

spacy/lexeme.pyi

@@ -20,7 +20,6 @@ class Lexeme:
     def vector_norm(self) -> float: ...
     vector: Floats1d
     rank: int
-    sentiment: float
     @property
     def orth_(self) -> str: ...
     @property

spacy/lexeme.pyx

@@ -173,19 +173,6 @@ cdef class Lexeme:
         def __set__(self, value):
             self.c.id = value
 
-    property sentiment:
-        """RETURNS (float): A scalar value indicating the positivity or
-            negativity of the lexeme."""
-        def __get__(self):
-            sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment", {})
-            return sentiment_table.get(self.c.orth, 0.0)
-
-        def __set__(self, float x):
-            if "lexeme_sentiment" not in self.vocab.lookups:
-                self.vocab.lookups.add_table("lexeme_sentiment")
-            sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment")
-            sentiment_table[self.c.orth] = x
-
     @property
     def orth_(self):
         """RETURNS (str): The original verbatim text of the lexeme

spacy/tests/README.md

@@ -10,12 +10,16 @@ Tests for spaCy modules and classes live in their own directories of the same na
 
 ## Table of contents
 
-1. [Running the tests](#running-the-tests)
+- [spaCy tests](#spacy-tests)
-2. [Dos and don'ts](#dos-and-donts)
+  - [Table of contents](#table-of-contents)
-3. [Parameters](#parameters)
+  - [Running the tests](#running-the-tests)
-4. [Fixtures](#fixtures)
+  - [Dos and don'ts](#dos-and-donts)
-5. [Helpers and utilities](#helpers-and-utilities)
+  - [Parameters](#parameters)
-6. [Contributing to the tests](#contributing-to-the-tests)
+  - [Fixtures](#fixtures)
+  - [Helpers and utilities](#helpers-and-utilities)
+    - [Constructing a `Doc` object manually](#constructing-a-doc-object-manually)
+    - [Other utilities](#other-utilities)
+  - [Contributing to the tests](#contributing-to-the-tests)
 
 ## Running the tests
 
@@ -40,7 +44,7 @@ py.test spacy/tests/tokenizer/test_exceptions.py::test_tokenizer_handles_emoji #
 
 To keep the behavior of the tests consistent and predictable, we try to follow a few basic conventions:
 
-- **Test names** should follow a pattern of `test_[module]_[tested behaviour]`. For example: `test_tokenizer_keeps_email` or `test_spans_override_sentiment`.
+- **Test names** should follow a pattern of `test_[module]_[tested behaviour]`. For example: `test_tokenizer_keeps_email`.
 - If you're testing for a bug reported in a specific issue, always create a **regression test**. Regression tests should be named `test_issue[ISSUE NUMBER]` and live in the [`regression`](regression) directory.
 - Only use `@pytest.mark.xfail` for tests that **should pass, but currently fail**. To test for desired negative behavior, use `assert not` in your test.
 - Very **extensive tests** that take a long time to run should be marked with `@pytest.mark.slow`. If your slow test is testing important behavior, consider adding an additional simpler version.

spacy/tests/doc/test_doc_api.py

@@ -381,7 +381,7 @@ def test_doc_api_serialize(en_tokenizer, text):
     assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
 
     new_tokens = Doc(tokens.vocab).from_bytes(
-        tokens.to_bytes(exclude=["sentiment"]), exclude=["sentiment"]
+        tokens.to_bytes()
     )
     assert tokens.text == new_tokens.text
     assert [t.text for t in tokens] == [t.text for t in new_tokens]

spacy/tests/doc/test_span.py

@@ -304,32 +304,6 @@ def test_span_similarity_match():
         assert span1.similarity(doc) == 0.0
         assert span1[:1].similarity(doc.vocab["a"]) == 1.0
 
-
-def test_spans_default_sentiment(en_tokenizer):
-    """Test span.sentiment property's default averaging behaviour"""
-    text = "good stuff bad stuff"
-    tokens = en_tokenizer(text)
-    tokens.vocab[tokens[0].text].sentiment = 3.0
-    tokens.vocab[tokens[2].text].sentiment = -2.0
-    doc = Doc(tokens.vocab, words=[t.text for t in tokens])
-    assert doc[:2].sentiment == 3.0 / 2
-    assert doc[-2:].sentiment == -2.0 / 2
-    assert doc[:-1].sentiment == (3.0 + -2) / 3.0
-
-
-def test_spans_override_sentiment(en_tokenizer):
-    """Test span.sentiment property's default averaging behaviour"""
-    text = "good stuff bad stuff"
-    tokens = en_tokenizer(text)
-    tokens.vocab[tokens[0].text].sentiment = 3.0
-    tokens.vocab[tokens[2].text].sentiment = -2.0
-    doc = Doc(tokens.vocab, words=[t.text for t in tokens])
-    doc.user_span_hooks["sentiment"] = lambda span: 10.0
-    assert doc[:2].sentiment == 10.0
-    assert doc[-2:].sentiment == 10.0
-    assert doc[:-1].sentiment == 10.0
-
-
 def test_spans_are_hashable(en_tokenizer):
     """Test spans can be hashed."""
     text = "good stuff bad stuff"

spacy/tests/matcher/test_matcher_api.py

@@ -50,8 +50,6 @@ def test_matcher_from_usage_docs(en_vocab):
 
     def label_sentiment(matcher, doc, i, matches):
         match_id, start, end = matches[i]
-        if doc.vocab.strings[match_id] == "HAPPY":
-            doc.sentiment += 0.1
         span = doc[start:end]
         with doc.retokenize() as retokenizer:
             retokenizer.merge(span)
@@ -61,7 +59,6 @@ def test_matcher_from_usage_docs(en_vocab):
     matcher = Matcher(en_vocab)
     matcher.add("HAPPY", pos_patterns, on_match=label_sentiment)
     matcher(doc)
-    assert doc.sentiment != 0
     assert doc[1].norm_ == "happy emoji"
 
 

spacy/tokens/doc.pxd

@@ -48,8 +48,6 @@ cdef class Doc:
 
     cdef TokenC* c
 
-    cdef public float sentiment
-
     cdef public dict activations
 
     cdef public dict user_hooks

spacy/tokens/doc.pyi

@@ -21,7 +21,6 @@ class Doc:
     spans: SpanGroups
     max_length: int
     length: int
-    sentiment: float
     activations: Dict[str, Dict[str, Union[ArrayXd, Ragged]]]
     cats: Dict[str, float]
     user_hooks: Dict[str, Callable[..., Any]]

spacy/tokens/doc.pyx

@@ -243,7 +243,6 @@ cdef class Doc:
         self.c = data_start + PADDING
         self.max_length = size
         self.length = 0
-        self.sentiment = 0.0
         self.cats = {}
         self.activations = {}
         self.user_hooks = {}
@@ -1270,7 +1269,6 @@ cdef class Doc:
         other.tensor = copy.deepcopy(self.tensor)
         other.cats = copy.deepcopy(self.cats)
         other.user_data = copy.deepcopy(self.user_data)
-        other.sentiment = self.sentiment
         other.has_unknown_spaces = self.has_unknown_spaces
         other.user_hooks = dict(self.user_hooks)
         other.user_token_hooks = dict(self.user_token_hooks)
@@ -1367,7 +1365,6 @@ cdef class Doc:
             "text": lambda: self.text,
             "array_head": lambda: array_head,
             "array_body": lambda: self.to_array(array_head),
-            "sentiment": lambda: self.sentiment,
             "tensor": lambda: self.tensor,
             "cats": lambda: self.cats,
             "spans": lambda: self.spans.to_bytes(),
@@ -1405,8 +1402,6 @@ cdef class Doc:
             for key, value in zip(user_data_keys, user_data_values):
                 self.user_data[key] = value
         cdef int i, start, end, has_space
-        if "sentiment" not in exclude and "sentiment" in msg:
-            self.sentiment = msg["sentiment"]
         if "tensor" not in exclude and "tensor" in msg:
             self.tensor = msg["tensor"]
         if "cats" not in exclude and "cats" in msg:

spacy/tokens/span.pyi

@@ -82,8 +82,6 @@ class Span:
     @property
     def tensor(self) -> FloatsXd: ...
     @property
-    def sentiment(self) -> float: ...
-    @property
     def text(self) -> str: ...
     @property
     def text_with_ws(self) -> str: ...

spacy/tokens/span.pyx

@@ -566,16 +566,6 @@ cdef class Span:
             return None
         return self.doc.tensor[self.start : self.end]
 
-    @property
-    def sentiment(self):
-        """RETURNS (float): A scalar value indicating the positivity or
-            negativity of the span.
-        """
-        if "sentiment" in self.doc.user_span_hooks:
-            return self.doc.user_span_hooks["sentiment"](self)
-        else:
-            return sum([token.sentiment for token in self]) / len(self)
-
     @property
     def text(self):
         """RETURNS (str): The original verbatim text of the span."""

spacy/tokens/token.pyi

@@ -79,8 +79,6 @@ class Token:
     @property
     def prob(self) -> float: ...
     @property
-    def sentiment(self) -> float: ...
-    @property
     def lang(self) -> int: ...
     @property
     def idx(self) -> int: ...

spacy/tokens/token.pyx

@@ -283,14 +283,6 @@ cdef class Token:
         """RETURNS (float): Smoothed log probability estimate of token type."""
         return self.vocab[self.c.lex.orth].prob
 
-    @property
-    def sentiment(self):
-        """RETURNS (float): A scalar value indicating the positivity or
-            negativity of the token."""
-        if "sentiment" in self.doc.user_token_hooks:
-            return self.doc.user_token_hooks["sentiment"](self)
-        return self.vocab[self.c.lex.orth].sentiment
-
     @property
     def lang(self):
         """RETURNS (uint64): ID of the language of the parent document's