def entityRecognition(client, text: str) -> None:
print(optionsTitle('--entity'))
translatedText: str
baseText: Dict[str, str]
response: Dict[str, list]
numberOfEntityFound: int
wordlist: List[dict]
if text:
translatedText = traduzir('en', text)
baseText = {"document": translatedText}
algoritimo = client.algo('StanfordNLP/NamedEntityRecognition/0.2.0')
response = algoritimo.pipe(baseText).result
numberOfEntityFound = len(response['sentences'])
wordlist = response['sentences'][numberOfEntityFound -
1]['detectedEntities']
if not wordlist:
print("Não foram encontradas nenhuma entidade.")
sys.exit(1)
else:
for name in wordlist:
print(
f'Nome: {name["word"]} \nEntidade: {traduzir("pt", name["entity"]).capitalize()}'
)
else:
print("Texto em branco.")
def getTags(client, arquivo: str) -> None:
print(optionsTitle('--tag'))
algoritimo = client.algo('nlp/AutoTag/1.0.1')
response: List[str] = algoritimo.pipe(arquivo).result
print(response)
def countWords(client, text: str) -> None:
print(optionsTitle('--count'))
algoritimo = client.algo('diego/WordCounter/0.1.0')
response: int = algoritimo.pipe(text).result
print(response)
def summarizeText(client, texto: str) -> None:
print(optionsTitle('--summarize'))
translatedText: str = traduzir('en', texto)
algoritimo = client.algo('nlp/Summarizer/0.1.8')
response: str = algoritimo.pipe(translatedText).result
print(traduzir('pt', response))
def getTags(client: 'Client', text: str) -> NoReturn:
from interface import texts
print(texts.optionsTitle('tag'))
algoritimo = client.algo('nlp/AutoTag/1.0.1')
response: List[str] = algoritimo.pipe(text).result
print(response)
def countWords(client: 'Client', text: str) -> NoReturn:
from interface import texts
print(texts.optionsTitle('count'))
algoritimo = client.algo('diego/WordCounter/0.1.0')
response: int = algoritimo.pipe(text).result
print(response)
def emailExtract(client, text: str) -> None:
print(optionsTitle('--email'))
algo = client.algo('cindyxiaoxiaoli/EmailExtractor/0.2.0')
response: List[str] = algo.pipe(text).result
if len(response) >= 0:
print(f'foram encontrados {len(response)} emails:\n')
for item in response:
print(f'{item}')
def summarizeText(client: 'Client', text: str) -> NoReturn:
from utils.translate import traduzir
from interface import texts
print(texts.optionsTitle('summarize'))
translatedText: str = traduzir('en', text)
algoritimo = client.algo('nlp/Summarizer/0.1.8')
response: str = algoritimo.pipe(translatedText).result
print(traduzir('pt', response))
def emailExtract(client: 'Client', text: str) -> NoReturn:
from interface import texts
print(texts.optionsTitle('email'))
algo = client.algo('cindyxiaoxiaoli/EmailExtractor/0.2.0')
response: List[str] = algo.pipe(text).result
if len(response) >= 0:
print(f'foram encontrados {len(response)} emails:\n')
for item in response:
print(f'{item}')
def feelingAnalisys(client, sentenca: str) -> None:
print(optionsTitle('--feeling'))
text: Dict[str, str] = {"sentence": traduzir('en', sentenca)}
algoritimo = client.algo('nlp/SocialSentimentAnalysis/0.1.4')
response: List[dict] = algoritimo.pipe(text).result
positive: int = math.floor(response[0]['positive'] * 100)
negative: int = math.floor(response[0]['negative'] * 100)
neutral: int = math.floor(response[0]['neutral'] * 100)
print("Positivi: {}%\nNegativo: {}%\nNeutro: {}%".format(
positive, negative, neutral))
def dateExtractor(client: 'Client', text: str) -> NoReturn:
from utils.translate import traduzir
from interface import texts
print(texts.optionsTitle('date'))
translatedText = traduzir('en', text)
algorithm = client.algo('PetiteProgrammer/DateExtractor/0.1.0')
response: List[str] = algorithm.pipe(translatedText).result
if len(response) > 0:
for date in response:
print(date)
else:
print('Nenhuma data encontrada')
def frequencyOfWords(client, texto: str) -> None:
print(optionsTitle('--frequency'))
arguments: List[str] = sys.argv
file: str = arguments[arguments.index('--file') + 1]
try:
wordCount: str = arguments[arguments.index('--frequency') + 1]
except:
print("Você não indicou o número de words analisadas!")
print(
f"analysis.py --file {file} --frequency [numero de words analisadas]")
sys.exit(1)
stopwords = [' a ', ' com ', ' da ', ' de ', ' do ',
' e ', ' ele ', ' em ', ' na ', ' no ',
' o ', ' para ', ' por ', ' que ', ' se ', ' sua ',
' um ', ' uma ', ' os ', ' ao ', ' mais ', ' quando ',
' como ', ' das ', ' vem ', ' ser ', ' foi ', ' pela '
]
stopwordsWithoutSpace = ['a', 'com', 'da', 'de', 'de',
'do', 'e', 'ele', 'em', 'na', 'no',
'o', 'para', 'por', 'que', 'se', 'sua',
'um', 'uma', 'os', 'ao' 'mais', 'quando',
'como', 'das', 'vem', 'ser', 'foi', 'pela'
]
textWithoutStopwords: str = removeStopwords(texto, stopwords)
wordList: List[str, bool] = [
textWithoutStopwords,
wordCount,
True,
True
]
algoritimo = client.algo('WebPredict/WordFrequencies/0.1.0')
response: List[dict] = algoritimo.pipe(wordList).result
responseWithoutStopwords = []
for wordInfo in response:
if wordInfo['word'] not in stopwordsWithoutSpace:
responseWithoutStopwords.append(wordInfo)
count: int = 1
for word in responseWithoutStopwords:
print(f"{count}ª Palavra mais comum: {word['word']}")
print(f"Frequência: {word['frequency']}\n")
count += 1
def feelingAnalisys(client: 'Client', text: str) -> NoReturn:
import math
from utils.translate import traduzir
from interface import texts
print(texts.optionsTitle('feeling'))
translatedText: Dict[str, str] = {"sentence": traduzir('en', text)}
algoritimo = client.algo('nlp/SocialSentimentAnalysis/0.1.4')
response: List[dict] = algoritimo.pipe(translatedText).result
positive: int = math.floor(response[0]['positive'] * 100)
negative: int = math.floor(response[0]['negative'] * 100)
neutral: int = math.floor(response[0]['neutral'] * 100)
print("Positivi: {}%\nNegativo: {}%\nNeutro: {}%".format(
positive, negative, neutral))
def frequencyOfWords(client: 'Client', text: str) -> NoReturn:
from matplotlib import pyplot as plt
import sys
from interface import texts
from utils.removeStopwords import removeStopwords
wordCount: str = input('nº de palavras a ser analisadas\n> ')
print(texts.optionsTitle('frequency'))
stopwords = [' a ', ' com ', ' da ', ' de ', ' do ',
' e ', ' ele ', ' em ', ' na ', ' no ',
' o ', ' para ', ' por ', ' que ', ' se ', ' sua ',
' um ', ' uma ', ' os ', ' ao ', ' mais ', ' quando ',
' como ', ' das ', ' vem ', ' ser ', ' foi ', ' pela ',
' eu ', ' te '
]
stopwordsWithoutSpace = ['a', 'com', 'da', 'de', 'de',
'do', 'e', 'ele', 'em', 'na', 'no',
'o', 'para', 'por', 'que', 'se', 'sua',
'um', 'uma', 'os', 'ao' 'mais', 'quando',
'como', 'das', 'vem', 'ser', 'foi', 'pela',
'eu', 'te'
]
textWithoutStopwords: str = removeStopwords(text, stopwords)
wordList: List[str, bool] = [
textWithoutStopwords,
wordCount,
True,
True
]
algoritimo = client.algo('WebPredict/WordFrequencies/0.1.0')
response: List[dict] = algoritimo.pipe(wordList).result
responseWithoutStopwords = []
for wordInfo in response:
if wordInfo['word'] not in stopwordsWithoutSpace:
responseWithoutStopwords.append(wordInfo)
print('Deseja exibir um gráfico com a frequencia de cada palavra? [s/n]')
graphicChoice = input('> ')
if graphicChoice == 's':
wordsData = []
frequencyData = []
for word in responseWithoutStopwords:
wordsData.append(word['word'])
frequencyData.append(word['frequency'])
plt.bar(wordsData, frequencyData)
plt.show()
else:
count: int = 1
for word in responseWithoutStopwords:
print(f"{count}ª Palavra mais comum: {word['word']}")
print(f"Frequência: {word['frequency']}\n")
count += 1
