class typing.Dict[str,] = None, use_subtrees: bool = False, granularity: str = '5-class', lazy: bool = False) → None[source]


Reads tokens and their sentiment labels from the Stanford Sentiment Treebank.

The Stanford Sentiment Treebank comes with labels from 0 to 4. "5-class" uses these labels as is. "3-class" converts the problem into one of identifying whether a sentence is negative, positive, or neutral sentiment. In this case, 0 and 1 are grouped as label 0 (negative sentiment), 2 is converted to label 1 (neutral sentiment) and 3 and 4 are grouped as label 2 (positive sentiment). "2-class" turns it into a binary classification problem between positive and negative sentiment. 0 and 1 are grouped as the label 0 (negative sentiment), 2 (neutral) is discarded, and 3 and 4 are grouped as the label 1 (positive sentiment).

Expected format for each input line: a linearized tree, where nodes are labeled by their sentiment.

The output of read is a list of Instance s with the fields:
tokens: TextField and label: LabelField
token_indexers : Dict[str, TokenIndexer], optional (default=``{“tokens”: SingleIdTokenIndexer()}``)

We use this to define the input representation for the text. See TokenIndexer.

use_subtrees : bool, optional, (default = False)

Whether or not to use sentiment-tagged subtrees.

granularity : str, optional (default = "5-class")

One of "5-class", "3-class", or "2-class", indicating the number of sentiment labels to use.

lazy : bool, optional, (default = False)

Whether or not instances can be read lazily.

text_to_instance(tokens: typing.List[str], sentiment: str = None) →[source]

We take pre-tokenized input here, because we don’t have a tokenizer in this class.

tokens : List[str], required.

The tokens in a given sentence.

sentiment ``str``, optional, (default = None).

The sentiment for this sentence.

An ``Instance`` containing the following fields:
tokens : TextField

The tokens in the sentence or phrase.

label : LabelField

The sentiment label of the sentence or phrase.