Python SDK

import lettria

nlp = lettria.NLP(api_key)
nlp.add_document([sentence_1, sentence_2])

for doc in nlp:
	for sentence in doc:
		print(sentence.token, sentence.pos)

To use our API, you will need a personal key, refered as API_KEY. Get your free API_KEY on your Dashboard.
Install using Python Software Developpement Kit :

pip install lettria

Check the official sources for more information and documentation on how to extract key informations using our SDK : https://github.com/Lettria/sdk-python

NLP class

NLP inherits from TextChunk.

NLP is a class designed to give access to relevant data at the different levels (document, sentence, subsentence) in an intuitive way. It allows to perform quick data exploration, manipulation and analysis.
It is also used to perform requests and can save and load the result as JSON objects.

When a response from the API is received it is stored in a hierarchy of classes:
NLP (all data) => Document => Sentence => Subsentence => Token
At each level direct access to inferior levels is possible i.e. nlp.sentences gives access to a list of all the Sentence in the current data, while nlp.documents[0].sentences gives only the Sentence of the first Document.

NLP is iterable and will yield Document instances.

Attributes / Properties

Name	Type	Description
documents	list of Document instances	List of all the Document instances.
sentences	list of Sentence instances	Direct access to all of the Sentences instances.
subsentences	list of Subsentence instances	Direct access to all of the Subsentence instances.
tokens	list of Token instances	Direct access to all Token in the subsentence
fields	list of string	List of all common properties accessible at all levels (token, pos etc.)
client	instance of Client	Client used for performing request to Lettria's API
Common properties	depends on property	Properties allowing access to specific data (pos, token etc.)

Methods

Data management

METHOD	DESCRIPTION
add_document()	Submits document to API
save_results()	Saves data from json file
load_results()	Loads data from json file
reset_data()	Erase data and reinitialise object
add_client()	Adds new client / api_key

add_document

add_document(document, skip_document = False, id=None, verbose=True)

Performs a request to lettria API using the API_KEY provided. Results are appended as an additional Document instance to the documents attribute.

Parameters:

Name	Type	Description	Optional
document	string or list of string	Data to be sent to the API	False
skip_document	bool	Whether to skip the document if there is a problem during processing	True
id	str	Id to identify the document, by default an incrementing integer is assigned.	True
verbose	bool	Whether to print additional statements about document processing.True

save_results

save_results(file = '')

Writes current results to a JSON file. If no file is specified the default path is results_X.json with X being next 'free' integer.

Parameters:

Name	Type	Description	Optional
file	string	Path of file to write in.	True

load_results

load_results(path = 'results_0', reset = False)

Loads results from a JSON file.

Parameters:

Name	Type	Description	Optional
file	string	Path of file to load.	True
reset	bool	Whether to erase current data.	True

reset_data

reset_data()

Erase all data inside NLP and reinitialise documents ids.

Parameters:

Name	Type	Description	Optional
file	string	Path of file to load.	True
reset	bool	Whether to erase current data.	True

add_client

add_client(client = None, api_key = None)

Replaces current client with provided one, or creates a new client using the provided api_key.

Parameters:

Name	Type	Description	Optional
client	instance of Client class	Client instance to replace the current one.	True
api_key	string	Key to use for the new client.	True

TextChunk

TextChunk is the base class of NLP, Document, Sentence and Subsentence.
It offers different methods that can be accessed through children classes.

Data analysis:

METHOD	DESCRIPTION
vocabulary()	Returns vocabulary from current data.
word_count()	Returns word count from current data.
word_frequency()	Returns word frequency of current data.
list_entities()	Returns dictionaries of detected entities by type.
get_emotion()	Returns emotion results at the specified hierarchical level
get_sentiment()	Returns sentiment results at the specified hierarchical level
word_sentiment()	Returns average sentiment for each word of the whole vocabulary
word_emotion()	Returns average emotion for each word of the whole vocabulary
meaning_sentiment()	Returns average sentiment for each meaning
meaning_emotion()	Returns average emotion for each meaning
filter_polarity()	Filters Sentence or Subsentence of the specified polarity
filter_emotion()	Filters Sentence or Subsentence of the specified emotions
filter_type()	Filters Sentence of the specified types
match_pattern()	Returns matches from given patterns.

vocabulary

vocabulary(filter_pos = None, lemma=False)

Returns vocabulary from current data with their associated POStag i.e. if a word appears both as a verb and a noun it will be in two tuples (word, 'V'), (word, 'N'). Allows filtering by POS tags.

Parameters:

Name	Type	Description	Optional
filter_pos	list of string	Tags to use for filtering.	True
lemma	string	Whether to use lemma or plain words.	True

Return:

Type	Description
list of tuple	List of unique tuples (token, POStag).

word_count

word_count(filter_pos = None, lemma=False):

Returns count of words from current data with their associated POStag i.e. if a word appears both as a verb and a noun it will be in two tuples (word, 'V'), (word, 'N'). Allows filtering by POS tags.

Parameters:

Name	Type	Description	Optional
filter_pos	list of string	Tags to use for filtering.	True
lemma	string	Whether to use lemma or plain words.	True

Return:

Type	Description
dictionary	dictionary of word counts { (token, POStag): occurences }.

word_frequency

word_frequency(filter_pos = None, lemma=False)

Returns words or lemma frequency, allows filtering by POS tag

Parameters:

Name	Type	Description	Optional
filter_pos	list of string	Tags to use for filtering.	True
lemma	bool	Whether to use lemma or plain words.	True

Return:

Type	Description
dictionary	Dictionary of word frequency

list_entities

list_entities()

Returns dictionaries of detected entities by type.

Return:

Type	Description
list of dictionary	List of dictionaries of different entities at the specified level.

get_emotion

get_emotion(granularity = 'sentence')

Returns emotion results, granularity defines whether to use emotion by sentence or by subsentence.

Parameters:

Name	Type	Description	Optional
granularity	string	Level at which emotions are analyzed. One of 'sentence' or 'subsentence'.	True

Return:

Type	Description
list of dict	List of dictionaries with emotions as keys and dict {'occurences','sum','average'} as values.

get_sentiment

get_sentiment(granularity = 'sentence')

Returns sentiment results, granularity defines whether to use sentiment by sentence or by subsentence.

Parameters:

Name	Type	Description	Optional
granularity	string	Level at which sentiments are analyzed. One of 'sentence' or 'subsentence'.	True

Return:

Type	Description
list of dict	List of dictionaries with polarity as keys and dict {'occurences','sum','average'} as values.

word_sentiment

word_sentiment(granularity = 'sentence', lemma = False, filter_pos = None, average=True)

Returns an average sentiment score for each word or lemma. For each sentence or subsentence (granularity parameter), the sentiment score is added to each of the words present. The scores are divided by the number of sentences or subsentences to get an average.

Parameters:

Name	Type	Description	Optional
granularity	string	Whether to use sentiment by 'sentence' or 'subsentence' for scoring.	True
lemma	bool	Whether to use lemma or plain words.	True
filter_pos	list of string	POStags to use for filtering.	True
average	bool	Whether to return average or list of values.	True

Return:

Type	Description
dictionary	Dictionary with words as keys and sentiment as value

{
	('patients', 'N'): -0.4917,
	('male', 'N'): -0.4275,
	('age', 'N'): -0.5167,
	('cure', 'N'): 0.6421
}

word_emotion

word_emotion(granularity = 'sentence', lemma = False, filter_pos = None, average=True)

Returns the average score for each emotion for each word or lemma in the vocabulary. For each sentence or subsentence (granularity parameter), the emotion scores are added to each of the words present. The scores are divided by the number of sentences or subsentences to get an average (or list of values if 'average' == False).

Parameters:

Name	Type	Description	Optional
granularity	string	Whether to use emotion by 'sentence' or 'subsentence' for scoring.	True
lemma	bool	Whether to use lemma or plain words.	True
filter_pos	list of string	POStags to use for filtering.	True
average	bool	Whether to return average or list of values.	True

Return:

Type	Description
dictionary	Dictionary with (words, POS tag) as keys and a dictionary with emotion scores as value.

{
	('patients', 'N'): {'surprise': 0.753, 'neutral': 0.445},
	('male', 'N'): {'neutral': 0.8},
	('surgery', 'N'): {'sadness': 0.79}
}

meaning_sentiment

meaning_sentiment(granularity='sentence', filter_meaning=None, average=True)

Returns average sentiment score for each meaning For each sentence or subsentence(granularity parameter), the sentiment score is added to each of the meaning present. The scores are divided by the number of sentences or subsentences to get an average. This can be used with custom meaning to get the sentiment associated with a particular meaning, for example 'customer service' or 'pricing' when analyzing customer reviews.
Parameters:

Name	Type	Description	Optional
granularity	string	Whether to use sentiment by 'sentence' or 'subsentence' for scoring.	True
filter_meaning	list of string	Filters results by list of meanings	True
average	bool	Whether to return average or list of values.	True

Return:

Type	Description
dictionary	Dictionary with meanings as keys and sentiment as value

meaning_emotion

meaning_emotion(granularity='sentence', filter_meaning=None, average=True)

Returns average emotion scores for each meaning. For each sentence or subsentence(granularity parameter), the score for each emotion is added to each of the meaning. The scores are divided by the number of sentences or subsentences to get an average. This can be used with custom meaning to get the emotion associated with a particular meaning, for example 'customer service' or 'pricing' when analyzing customer reviews.

Parameters:

Name	Type	Description	Optional
granularity	string	Whether to use emotion by 'sentence' or 'subsentence' for scoring.	True
filter_meaning	list of string	Filters results by list of meanings	True
average	bool	Whether to return average or list of values.	True

Return:

Type	Description
dictionary	Dictionary with meanings as keys and sentiment as value

filter_polarity

filter_polarity(polarity, granularity='sentence')

Filters Sentence or Subsentence of the specified polarity.

Parameters:

Name	Type	Description	Optional
polarity	string or list of string	Polarity, 'neutral', 'positive', 'negative'.	False
granularity	string	Whether to use sentiment by 'sentence' or 'subsentence' for scoring.	True

Return:

Type	Description
list of instances of Sentence or Subsentence	List of instances of objects with the specified polarity.

filter_emotion

filter_emotion(emotions, granularity='sentence')

Filters Sentence of the specified emotions.

Parameters:

Name	Type	Description	Optional
emotions	string or list of string	Emotions to filter, one of 'joy', 'love', 'surprise', 'anger', 'sadness', 'fear' or 'neutral'.	False
granularity	string	Whether to use sentiment by 'sentence' or 'subsentence' for scoring.	True

Return:

Type	Description
list of instances of Sentence or Subsentence	List of instances of objects with the specified emotion.

filter_type

filter_type(sentence_type)

Filters Sentence of the specified emotions.

Parameters:

Name	Type	Description	Optional
sentence_type	string or list of string	Types to filter, one of 'assert', 'command', 'question_open', 'question_closed'.	False

Return:

Type	Description
list of instances of Sentence	List of instances of Sentence with the specified type.

match_pattern

match_pattern(self, patterns_json, level = None, print_tree=False, skip_errors=False)

Match given pattern (either Token Pattern or Dependency Pattern) on the current TextChunk object.
The 'level' argument specifies on which level the matching should be done, i.e. on the document level (returns matches per document), on the sentence or subsentence level. The default level is one level below in the hierarchy, document for NLP class, sentence for Document class and subsentence for Sentence class.
For more information on patterns look at the dedicated section: Patterns.

Parameters:

Name	Type	Description	Optional
patterns_json	dictionary	Token Pattern or Dependency Pattern	False
level	string	Level on which matching is done, one of 'document', 'sentence', 'subsentence'	True
print_tree	bool	Whether to print the dependency tree for dependency patterns.	True
skip_errors	bool	Whether to skip errors and continue matching.	True

Return:

Type	Description
list of tuple	List of tuple (TextChunk object, match dictionary)

Document Class

Document inherits from TextChunk.

Document stores the information for a document (for example an online review for a product or a news article). The class is iterable and will yield instances of Sentence.

Attributes / Properties

Name	Type	Description
sentences	list of Sentence instances	List of Sentences of the document.
subsentences	list of Subsentence instances	Direct access to list of Subsentence for the document.
id	str	Id of document, by default sequential integer if not provided.
common properties	depends on property	Properties allowing access to specific data (pos, token etc.).

Sentence Class

Sentence inherits from TextChunk.

Sentence stores data for a sentence. Sentences are delimited automatically from the input raw text. For longer and more complicated sentences it can be advantageous to further cut the sentences into subsentences.

Sentence is iterable and will yield instances of Token class

Attributes / Properties

Name	Type	Description
subsentences	list of Subsentence instances	List of Subsentence in the sentence
tokens	list of Token instances	List of Token in the sentence
common properties	depends on property	Properties allowing access to specific data (pos, token etc.)

Subsentence Class

Subsentence inherits from TextChunk.

Subsentence stores data relative to a part of a sentence. For longer and more complicated sentences it can be advantageous to cut it in multiple pieces to have a more detailed analysis.

For example: I liked the park but it was raining and the weather was cold would be cut into:

I liked the park but it was raining and the weather was cold

In this case it allows to perform more precise sentiment analysis than assigning a score to the whole sentence.

Subsentence is iterable and will yield instances of Token class.

Attributes / Properties

Name	Type	Description
tokens	list of Token instances	List of Token in the subsentence
common properties	depends on property	Properties allowing access to specific data (pos, token etc.)

Token Class

Token stores data relative to a specific token. Tokens are delimited according to our Tokenizer.

Attributes / Properties

Name	Type	Description
common properties	depends on property	Properties allowing access to specific data (pos, token etc.)

Common properties

These properties are accessible at all analysis levels : NLP, Document, Sentence, Subsentence, Token.

All properties have a _flat variant (token_flat) which flatten recursively the return.

Name	type	Description
str	String	Returns sentence as string
original_text	String	Returns the original token in the input text before modification from the tokenization.
token	String	Returns token
lemma	String	Returns lemma
lemma_detail	String	Returns unmerged lemma
pos	String	Returns POS (Part-Of-Speech) tags
pos_detail	String	Returns unmerged POS (Part-Of-Speech) tags
dep	String	Returns dependency relations
morphology	String	Returns morphological features
language	String	Returns detected language
meaning	List of Tuples	Returns meanings as tuples (SUPER, SUB)
emotion	Tuple	Returns emotion as tuple (Type, score)
sentiment	Dictionary	Returns sentiment with positive, negative and total values
sentiment_ml	Dictionary	Returns sentiment of ml_model without further fine tuning
sentiment_target	Tuple	Returns 'target' of words with strong sentimental meaning
sentence_type	String	Returns type of sentence
coreference	String	Returns reference of token if it exists
synthesis	Dictionary	Returns synthesis object

Sentiment Class

<!> Sentiment is deprecated as 5.0.2 and may be removed in future releases, all methods are now available at all levels through the functional interface, cf TextChunk. <!>

Sentiment provides methods to perform some specific sentiment and emotion analysis. It takes as input an instance of NLP and uses it to retrieve data.

import lettria
from lettria import Sentiment
nlp = lettria.NLP(api_key)
nlp.add_document(['sentence 1', 'sentence 2'])

sentiment = Sentiment(nlp)

Attributes / Properties

Name	Type	Description
nlp	NLP class	Instance of NLP class

Methods

Name	Description
get_emotion()	Returns emotion results at the specified hierarchical level
get_sentiment()	Returns sentiment results at the specified hierarchical level
word_sentiment()	Returns average sentiment for each word of the whole vocabulary
meaning_sentiment()	Returns average sentiment for each meaning
filter_polarity()	Filters Sentence or Subsentence of the specified polarity
filter_emotion()	Filters Sentence or Subsentence of the specified emotions

get_emotion

get_emotion(level='document')

Returns emotion results at the specified hierarchical level. For example get_emotion(level='document') will concatenate emotion at the document level and return a list of emotions for each Document.

Parameters:

Name	Type	Description	Optional
level	string	Hierarchical level at which results are concatened. One of 'global', 'document', 'sentence', 'subsentence'.	True

Return:

Type	Description
list of dict	List of dictionaries with emotions as keys and dict {'occurences','sum','average'} as values.

get_sentiment

get_sentiment(level='document')

Returns sentiment results at the specified hierarchical level For example get_sentiment(level='document') will concatenate sentiment at the document level and return a list of emotions for each Document.

Parameters:

Name	Type	Description	Optional
level	string	Hierarchical level at which results are concatened. One of 'global', 'document', 'sentence', 'subsentence'.	True

Return:

Type	Description
list of dict	List of dictionaries with polarity as keys and dict {'occurences','sum','average'} as values.

word_sentiment

word_sentiment(granularity = 'sentence', lemma = False, filter_pos = None)

Returns an average sentiment score for each word or lemma in the data. For each sentence or subsentence, the sentiment score is added to each of the words in the phrase. The scores are divided by the number of occurences to get an average.

Parameters:

Name	Type	Description	Optional
granularity	string	Whether to use sentiment by 'sentence' or 'subsentence' for scoring.	True
lemma	bool	Whether to use lemma or plain words.	True
filter_pos	list of string	POStags to use for filtering.	True

Return:

Type	Description
dictionary	Dictionary with words as keys and sentiment as value

meaning_sentiment

meaning_sentiment(granularity='sentence', filter_meaning=None)

Returns average sentiment score for each meaning For each sentence or subsentence, the sentiment score is added to each of the meaning in the phrase. The scores are divided by the number of occurences to get an average. For example, this can be used with custom meaning to get a sentiment associated with customer service or pricing when analyzing reviews.

Parameters:

Name	Type	Description	Optional
granularity	string	Whether to use sentiment by 'sentence' or 'subsentence' for scoring.	True
filter_meaning	list of string	Filters results by list of meanings	True

Return:

Type	Description
dictionary	Dictionary with meanings as keys and sentiment as value

filter_polarity

filter_polarity(polarity, granularity='sentence')

Filters Sentence or Subsentence of the specified polarity.

Parameters:

Name	Type	Description	Optional
polarity	string	Polarity, one of 'neutral', 'positive', 'negative'.	False
granularity	string	Whether to use sentiment by 'sentence' or 'subsentence' for scoring.	True

Return:

Type	Description
list of instances of Sentence or Subsentence	List of instances of objects with the specified polarity.

filter_emotion

filter_emotion(emotions, granularity='sentence')

Filters Sentence of the specified emotions.

Parameters:

Name	Type	Description	Optional
emotions	list of string	Emotions to filter.	False
granularity	string	Whether to use sentiment by 'sentence' or 'subsentence' for scoring.	True

Return:

Type	Description
list of instances of Sentence or Subsentence	List of instances of objects with the specified emotions.

Client Class

Client used to perform requests to our API.

Attributes / Properties

Name	Type	Description
key	string	The API_KEY that will be used by the client.

Methods

METHOD	DESCRIPTION
request()	Send a request to our API

request

request(text)

Performs a request to lettria API using the API_KEY provided.

Parameters:

Name	Type	Description	Optional
text	string	Text data to be sent to the API	False

Return:

Type	Description
list of dictionary	Each of these objects represents the informations collected for a sentence.

Patterns

Patterns let you find documents, sentences or tokens according to certain rules that will describe token attributes and relations between tokens. Attributes refer to information about a token like postag, raw text, dependency, entity type ...
Patterns follow a dictionary structure and can be loaded from json files.

Token Patterns

Token patterns are simple patterns that do not use dependency parsing. They consist of a sequence of attributes related rules.

Attributes

Attributes are the properties of a token after analysis by the comprehension API. By defining an attribute in a pattern, only tokens that match the specific attribute will be matched.

Attribute	Description
ORTH	Text of the token.
TEXT	Text of the token.
LOWER	Text of the token in lowercase.
LEMMA	Lemma of the token.
POS	Part-Of-Speech tag of the token.
DEP	Dependency tag of the token.
ENT_TYPE	Entity type of the token (see NER API).
CATEGORY_SUPER	Category "SUPER" of the token (see Meaning API).
CATEGORY_SUB	Category "SUB" of the token (see Meaning API).
LENGTH	Length of the token.
IS_ALPHA	Token is composed of alphabectics characters.
IS_ASCII	Token is composed of ASCII characters.
IS_DIGIT	Token is composed of digits.
IS_LOWER	Token is in lowercase.
IS_UPPER	Token is in uppercase.
IS_TITLE	Token is in titlecase.
IS_PUNCT	Token is punctuation.
IS_SPACE	Token is space.
IS_SENT_START	Token is the first in the sentence.
LIKE_NUM	Token is a number.
LIKE_URL	Token has entity type URL.
LIKE_EMAIL	Token has entity type email.
OP	Operator to modify matching behavior.

Modifiers

Each attribute can map either to a single value or to a dictionary that allows modifiers for more complex behaviors.

Attribute Modifier	Description
IN	Attribute value is a member of this list.
NOT_IN	Attribute value is not amember of this list.
ISSUBSET	Attribute value is a subset (part of) this list
ISSUPERSET	Attribute value is a superset of this list.
==, >, <, >=, <=	For integer comparisons, attribute value is equal, greater or lower.

Operators

Operators work similarly as regular expressions operators, they allow to choose how often should a token be matched.

Operator	Description
?	Pattern step is optional and can match 0 or 1 token.
+	Pattern can match 1 or more tokens.
*	Pattern can match 0 or more tokens.
!	Pattern is negated, it must match 0 time.
.	Default operator, pattern should match 1 token.

## Token Pattern
patterns = {
{
    "patients":
        [
            {"POS":{"IN":["CD", "ENTITY"]}},
            {"POS":{"IN":["RB", "JJ", "PUNCT", "ENTITY"]}, "OP":"*"},
            {"LEMMA":"patient"}
        ],
    "date":
        [
            {"ENT_TYPE":"duration"}
        ]
}

text = "3416 consecutive patients were reviewed and 1476 finally enrolled (65.9 ± 20.9 years, 57.3% male). 76 (5.1%) patients had NAEs. Of 444 patients, 76% were male. They had a mean age of 69 ± 10 years."
nlp.add_document(text)

for s, matches in nlp.match_pattern(patterns, level='sentence'):
    print(s.str)
	print(matches)

3416 consecutive patients were reviewed and 1476 finally enrolled (65.9 ± 20.9 years, 57.3% male). 
{'patients': [[3416, consecutive, patients]], 'date': [[20.9 years]]}
76 (5.1%) patients had NAEs.
{'patients': [[76, (, 5.1%, ), patients]]}
Of 444 patients, 76% were male.
{'patients': [[444, patients]]}
They had a mean age of 69 ± 10 years.
{'date': [[10 years]]}

Dependency Patterns

Dependency patterns use dependency parsing which construct a grammatical tree of the sentence to allow complex matching patterns.
Attributes matching is similar to Token Patterns (Only exception is that for operators only "?" is available) but there are also relation operators specific to dependency matching.
Matching between the pattern and the sentence does not use the order of token (like for Token Patterns) but the dependency relations between tokens.

A Dependency Pattern consist of a list of dictionary formated in this way:

Name	Description
LEFT_ID	Name of the left node in the relation, it must have been defined in an earlier node.
REL_OP	Operator that describes the relation between left and right nodes.
RIGHT_ID	Name of the right node in the relation (the current node).
RIGHT_ATTRS	The attributes that must match with the right node, they are defined similarly as for Token Patterns.

All fields must be completed except for the root node which only needs 'RIGHT_ID' and 'RIGHT_ATTRS' fields. Each pattern must have one root node.

Relation Operators

Relation Operator	Description
<	A is a direct dependant of B.
>	A is the immediate head of B.
<<	A is a dependant of B directly or indirectly.
>>	A is a head of B directly or indirectly.
.	A token directly precedes B: A.idx == B.idx - 1.
.*	A token is located before B: A.idx < B.idx.
;	A token directly follows B: A.idx == B.idx + 1.
;*	A token is located after B: A.idx > B.idx.
$+	A is a sibling of B (same parent/head) and is located directly before B: A.idx == B.idx - 1.
$-	A is a sibling of B (same parent/head) and is located directly after B: A.idx == B.idx + 1.
$++	A is a sibling of B and is located before B: A.idx < B.idx.
$--	A is a sibling of B and is located after B: A.idx > B.idx.

Dependency matching should not be used on Subsentence since they don't have a complete dependency tree.

## Dependency Pattern
patterns = {
    "fruits":
        [
            {
            "RIGHT_ID": "rootnode",
            "RIGHT_ATTRS": {"LEMMA": {"IN":["banana", "apple", "pear"]}}
            },
            {
            "LEFT_ID": "rootnode",
            "REL_OP": ">",
            "RIGHT_ID": "num",
            "RIGHT_ATTRS": {"POS": "CD", "DEP":"nummod"}
            }
        ]
}

text = "I have 48 bananas, 32 pears and one apple."
nlp.add_document(text)

for doc, matches in nlp.match_pattern(patterns):
    print(matches)

{'fruits': [[bananas, 48], [pears, 32], [apple, one]]}

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Python SDK

NLP class

Attributes / Properties

Methods

add_document

save_results

load_results

reset_data

add_client

TextChunk

vocabulary

word_count

word_frequency

list_entities

get_emotion

get_sentiment

word_sentiment

word_emotion

meaning_sentiment

meaning_emotion

filter_polarity

filter_emotion

filter_type

match_pattern

Document Class

Attributes / Properties

Sentence Class

Attributes / Properties

Subsentence Class

Attributes / Properties

Token Class

Attributes / Properties

Common properties

Sentiment Class

Attributes / Properties

Methods

get_emotion

get_sentiment

word_sentiment

meaning_sentiment

filter_polarity

filter_emotion

Client Class

Attributes / Properties

Methods

request

Patterns

Token Patterns

Dependency Patterns