def test_train_charlm_load_use_classifier(results_base_path, tasks_base_path):
corpus = flair.datasets.ClassificationCorpus((tasks_base_path / 'imdb'))
label_dict = corpus.make_label_dictionary()
embedding = FlairEmbeddings('news-forward-fast')
document_embeddings = DocumentRNNEmbeddings([embedding], 128, 1, False, 64,
False, False)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path, max_epochs=2, shuffle=False)
sentence = Sentence('Berlin is a really nice city.')
for s in model.predict(sentence):
for l in s.labels:
assert (l.value is not None)
assert (0.0 <= l.score <= 1.0)
assert (type(l.score) is float)
loaded_model = TextClassifier.load((results_base_path / 'final-model.pt'))
sentence = Sentence('I love Berlin')
sentence_empty = Sentence(' ')
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
shutil.rmtree(results_base_path)
def test_train_load_use_classifier_flair(results_base_path, tasks_base_path):
corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb",
label_type="topic")
label_dict = corpus.make_label_dictionary(label_type="topic")
flair_document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
[flair_embeddings], 128, 1, False, 64, False, False)
model: TextClassifier = TextClassifier(
document_embeddings=flair_document_embeddings,
label_dictionary=label_dict,
label_type="topic",
multi_label=False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path, max_epochs=2, shuffle=False)
sentence = Sentence("Berlin is a really nice city.")
model.predict(sentence)
for label in sentence.labels:
assert label.value is not None
assert 0.0 <= label.score <= 1.0
assert type(label.score) is float
del trainer, model, corpus, flair_document_embeddings
loaded_model = TextClassifier.load(results_base_path / "final-model.pt")
sentence = Sentence("I love Berlin")
sentence_empty = Sentence(" ")
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
# clean up results directory
shutil.rmtree(results_base_path)
del loaded_model
def test_train_load_use_classifier_with_sampler(results_base_path,
tasks_base_path):
corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb")
label_dict = corpus.make_label_dictionary()
model: TextClassifier = TextClassifier(document_embeddings,
label_dict,
multi_label=False)
trainer = ModelTrainer(model, corpus)
trainer.train(
results_base_path,
max_epochs=2,
shuffle=False,
sampler=ImbalancedClassificationDatasetSampler,
)
sentence = Sentence("Berlin is a really nice city.")
for s in model.predict(sentence):
for l in s.labels:
assert l.value is not None
assert 0.0 <= l.score <= 1.0
assert type(l.score) is float
del trainer, model, corpus
loaded_model = TextClassifier.load(results_base_path / "final-model.pt")
sentence = Sentence("I love Berlin")
sentence_empty = Sentence(" ")
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
# clean up results directory
shutil.rmtree(results_base_path)
del loaded_model
def main(FIN, FOUT):
print("Reading in data.")
df = pd.read_csv(FIN)
count = 0 # global count
# filter dataframe for only USA tweets
df = df[df['place_country_code'] == 'US'].fillna('None')
df = df[df['language'] == 'en']
# load classifier
classifier = TextClassifier.load('sentiment')
print('Model has been loaded from flair.')
try:
os.mkdir(FOUT)
except:
pass
print('Running Script.')
for row in (df[[
'created_at', 'place_full_name', 'language', 'mentions',
'hashtags', 'clean_text'
]].iterrows()):
tweet, count = run_stack(count, row[1]['created_at'],
row[1]['place_full_name'],
row[1]['language'], row[1]['mentions'],
row[1]['hashtags'], row[1]['clean_text'])
dump_tweet(FOUT + '/sentiment_tweets.json', tweet)
print('Script has finished.')
def test_train_load_use_classifier_with_prob(results_base_path,
tasks_base_path):
corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb")
label_dict = corpus.make_label_dictionary()
word_embedding: WordEmbeddings = WordEmbeddings("turian")
document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
[word_embedding], 128, 1, False, 64, False, False)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path,
EvaluationMetric.MICRO_F1_SCORE,
max_epochs=2,
shuffle=False)
sentence = Sentence("Berlin is a really nice city.")
for s in model.predict(sentence, multi_class_prob=True):
for l in s.labels:
assert l.value is not None
assert 0.0 <= l.score <= 1.0
assert type(l.score) is float
loaded_model = TextClassifier.load(results_base_path / "final-model.pt")
sentence = Sentence("I love Berlin")
sentence_empty = Sentence(" ")
loaded_model.predict(sentence, multi_class_prob=True)
loaded_model.predict([sentence, sentence_empty], multi_class_prob=True)
loaded_model.predict([sentence_empty], multi_class_prob=True)
# clean up results directory
shutil.rmtree(results_base_path)
def main():
db_name = 'restaurants.db'
years = [
'2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018',
'2019', '2020'
]
restaurants = ['Tibits', 'Mildreds Soho', 'By Chloe']
classifier = TextClassifier.load('sentiment')
for restaurant in restaurants:
print('restaurant', restaurant)
for year in years:
print('year', year)
sentiment_scores = calcualte_sentiment_for_given_restaurant_year(
db_name, classifier, restaurant, year)
number_of_positive = 0
number_of_negative = 0
for score in sentiment_scores:
if score == 1:
number_of_positive = number_of_positive + 1
else:
number_of_negative = number_of_negative + 1
print('positive', number_of_positive)
print('negative', number_of_negative * -1)
import pandas as pd
import streamlit as st
from flair.data import Sentence
from flair.models import TextClassifier
from twitterscraper import query_tweets
import sys
sys.path.append('/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages')
# Set page title
st.title('Twitter Sentiment Analysis')
# Load classification model
with st.spinner('Loading classification model...'):
classifier = TextClassifier.load('models/best-model.pt')
# Preprocess function
allowed_chars = ' AaBbCcDdEeFfGgHhIiJjKkLlMmNnOoPpQqRrSsTtUuVvWwXxYyZz0123456789~`!@#$%^&*()-=_+[]{}|;:",./<>?'
punct = '!?,.@#'
maxlen = 280
def preprocess(text):
# Delete URLs, cut to maxlen, space out punction with spaces, and remove unallowed chars
return ''.join([' ' + char + ' ' if char in punct else char for char in [char for char in re.sub(r'http\S+', 'http', text, flags=re.MULTILINE) if char in allowed_chars]])
### SINGLE TWEET CLASSIFICATION ###
st.subheader('Single tweet classification')
# Get sentence input, preprocess it, and convert to flair.data.Sentence format
tweet_input = st.text_input('Tweet:')
)
# 5. create the text classifier
classifier = TextClassifier(document_embeddings, label_dictionary=label_dict)
# 6. initialize the text classifier trainer
trainer = ModelTrainer(classifier, corpus)
# 7. start the training
trainer.train('resources/taggers/ag_news',
learning_rate=0.1,
mini_batch_size=32,
anneal_factor=0.5,
patience=5,
max_epochs=150)
# 8. plot training curves (optional)
from flair.visual.training_curves import Plotter
plotter = Plotter()
plotter.plot_training_curves('resources/taggers/ag_news/loss.tsv')
plotter.plot_weights('resources/taggers/ag_news/weights.txt')
classifier = TextClassifier.load('resources/taggers/ag_news/final-model.pt')
# create example sentence
sentence = Sentence('France is the current world cup winner.')
# predict tags and print
classifier.predict(sentence)
print(sentence.labels)
reproject_words_dimension=256,
)
# 5. create the text classifier
classifier = TextClassifier(document_embeddings, label_dictionary=label_dict)
# 6. initialize the text classifier trainer
trainer = ModelTrainer(classifier, corpus)
# 7. start the training
trainer.train('resources/classifiers',
learning_rate=0.1,
mini_batch_size=32,
anneal_factor=0.5,
patience=5,
max_epochs=150)
# 以下、モデルを読み込んで実行してみる
classifier = TextClassifier.load('resources/classifiers/best-model.pt')
# create example sentence
sentence = Sentence("現実を受け入れて生きるしかない", use_tokenizer=japanese_tokenizer)
print(sentence.to_tokenized_string())
# predict class and print
classifier.predict(sentence)
label_dict = sentence.to_dict()["labels"][0]
label_dict["confidence"] if label_dict["value"] == "__label__O" else 0
#apply document LSTM to the stacked embeddings
document_embeddings = DocumentRNNEmbeddings(
word_embeddings,
# hidden_size=512,
# reproject_words=True,
# reproject_words_dimension=256,
)
#build model
classifier = TextClassifier(document_embeddings, label_dictionary=corpus.make_label_dictionary(), multi_label=False)
trainer = ModelTrainer(classifier, corpus)
#specify parameters and train model
trainer.train(PATH/'models/', max_epochs=3,checkpoint=True, learning_rate=1e-1)
classifier = TextClassifier.load('/content/drive/My Drive/emnlp/models/best-model.pt')
"""## Dev Set Prediction"""
dev_folder = "" # if not adjust these variables accordingly
dev_template_labels_file = ""
task_SLC_output_file = ""
def read_articles_from_file_list(folder_name, file_pattern="*.txt"):
file_list = glob.glob(os.path.join(folder_name, file_pattern))
article_id_list, sentence_id_list, sentence_list = ([], [], [])
import datetime as dt
import re
import pandas as pd
import streamlit as st
from flair.data import Sentence
from flair.models import TextClassifier
# Set page title
st.title('Sentiment Analysis')
# Load classification model
with st.spinner('Loading classification model...'):
classifier = TextClassifier.load(
'/Users/mengyu/Desktop/engineering/models/best-model.pt')
# Preprocess function
allowed_chars = ' AaBbCcDdEeFfGgHhIiJjKkLlMmNnOoPpQqRrSsTtUuVvWwXxYyZz0123456789~`!@#$%^&*()-=_+[]{}|;:",./<>?'
punct = '!?,.@#'
maxlen = 280
def preprocess(text):
# Delete URLs, cut to maxlen, space out punction with spaces, and remove unallowed chars
return ''.join([
' ' + char + ' ' if char in punct else char for char in [
char
for char in re.sub(r'http\S+', 'http', text, flags=re.MULTILINE)
if char in allowed_chars
]
])
return thevalence
#Surely I missed the builtin method that just returns the value?
except Exception:
print("An exception occurred. Text was not passed to get_valence")
return 'n/a'
def plotone_df(df):
plt.hist(df['valence'], color='blue', edgecolor='black')
#plt.tight_layout()
plt.show()
print_intro()
classifier = TextClassifier.load('en-sentiment')
num = 0
for df in dfs:
threelines()
print("Preparing valence for df %d - this may take a second" % num)
#print("Preparing valence for df %s "%str(dfs.index(df)))
df['valence'] = df['text'].apply(get_valence)
#df.reset_index(drop=True)
#plt.hist(df['valence'], color='blue', edgecolor='black')
# plt.tight_layout()
#plt.show()
num += 1
#plotone_df(df)
#print(df['valence'].dtypes)
from flask import Flask, jsonify, request
from fastai.text import *
from flair.models import TextClassifier
from flair.data import Sentence
import json
import heapq
application = Flask(__name__)
classifier_sentiment = TextClassifier.load('./models/best-model.pt')
@application.route("/")
def hello():
return "Hello World!"
@application.route('/classify', methods=['POST'])
def post_tasks():
return_object = []
data = json.loads(request.data)
df = pd.DataFrame(data)
items = TextList.from_df(df[['sample']])
learn = load_learner('./models', 'export.pkl', test=items)
preds = learn.get_preds(ds_type=DatasetType.Test)[0].tolist()
preds = [get_classes(item) for item in preds]
for indx, sample in enumerate(data, start=0):
sample["categories"] = preds[indx]
print(sample)
return_object.append(sample)
return jsonify(return_object)
import pandas as pd
from flair.models import TextClassifier
from flair.data import Sentence
import sys
from flair.models import SequenceTagger
import webbrowser
import re
import os
debug = False
play_tags = ['ok','play','yes','sure','like','love','awesome','nice','yep','yeah','good']
retry_tags = ['no','next','shuffle','hate','dislike','another','nope','nay','jeez','nah','ugh','not']
#model loadings
tagger = SequenceTagger.load('pos')
mood = TextClassifier.load('en-sentiment')
classifier = TextClassifier.load(sys.argv[1])
df = pd.read_pickle('./data/music.pkl')
df = df.loc[df['valence'] != '0.0']
genres = set(df['genre'])
mood_history = []
current_genre = False
def debug_print(*objects):
global debug
if(debug):
print(objects)
import streamlit as st
import pandas as pd
import numpy as np
from flair.models import TextClassifier
from flair.data import Sentence
##checkpt: Jun6 8:31pm done prediction and reading dataframe!
new_data_folder = './gdp_benchmark_classifier//'
finetuned_classifier = TextClassifier.load(new_data_folder + 'best-model.pt')
def finetuned_model_predictions(input_file_path, col_text,
finetuned_classifier, output_file_path):
'''Makes Sentiment Predictions on unannotated data points contained in the input csvfile by loading the user-defined classifier.
Exports the csvfile by adding new columns and filling in results from model predictions.
'''
if col_text.isdigit(): # if no text header
unannotated_df = pd.read_csv(input_file_path, header=None)
col_text = int(
col_text
) ## indexing works after turning input string into integer !!
else:
unannotated_df = pd.read_csv(input_file_path)
## drop some duplicated rows
#unannotated_df = unannotated_df.drop_duplicates(col_text)
## add new columns to export predictions
## modified on May28 to export predict_prob for less likely labels as well
def __init__(self, ckpt=CKPT, name2nats=DICT):
self.classifier = TextClassifier.load(ckpt)
self.name2nats = self.construct(name2nats)
consumer_key = 'XXXX'
consumer_secret = 'XXXX'
access_token = 'XXXX'
access_token_secret = 'XXXX'
auth = tw.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tw.API(auth, wait_on_rate_limit=True)
# Set page title
st.title('Twitter Sentiment Analysis')
# Load classification model
with st.spinner('Loading classification model...'):
classifier = TextClassifier.load('model-saves/my_fine_tuned_bert1.pt')
# Preprocess function
allowed_chars = ' AaBbCcDdEeFfGgHhIiJjKkLlMmNnOoPpQqRrSsTtUuVvWwXxYyZz0123456789~`!@#$%^&*()-=_+[]{}|;:",./<>?'
punct = '!?,.@#'
maxlen = 280
def preprocess(text):
# Delete URLs, cut to maxlen, space out punction with spaces, and remove unallowed chars
return ''.join([
' ' + char + ' ' if char in punct else char for char in [
char
for char in re.sub(r'http\S+', 'http', text, flags=re.MULTILINE)
if char in allowed_chars
]
def predict_labels(data, pretrained_model):
classifier = TextClassifier.load(pretrained_model)
data_to_classify, sub_outcomes = [], []
with open(data, 'r') as d:
instances = []
for i in d.readlines():
if i != '\n':
i = i.split()
instances.append(i)
else:
# if instances:
instances_copy = instances.copy()
data_to_classify.append(instances_copy)
outcome, sub_instances, l = '', [], 0
_outcomes_ = ()
#print(instances_copy)
for x in range(len(instances_copy)):
if x == l:
x_str = instances_copy[x][1]
# if x_str != 'O':
if x_str.startswith('B') or x_str.startswith('I'):
outcome = instances_copy[x][0]
if x == len(instances_copy) - 1:
if str(outcome.strip()) != 'nan':
sent = Sentence(outcome)
classifier.predict(sent)
sub_instances.append('{}:{}'.format(
outcome.strip(), sent.labels[0].value))
else:
for y in range(x + 1, len(instances_copy)):
if not instances_copy[y][1].startswith(
'B'
) and instances_copy[y][1] != 'O':
outcome += ' {}'.format(
instances_copy[y][0])
outcome = outcome.strip()
if y == len(instances_copy) - 1:
outcome_copy = outcome
if str(outcome_copy.strip()
) != 'nan':
sent = Sentence(outcome_copy)
classifier.predict(sent)
sub_instances.append(
'{}:{}'.format(
outcome_copy.strip(),
sent.labels[0].value))
outcome = ''
l = y
else:
if outcome:
outcome_copy = outcome
if str(outcome_copy.strip()
) != 'nan':
sent = Sentence(outcome_copy)
classifier.predict(sent)
sub_instances.append(
'{}:{}'.format(
outcome_copy.strip(),
sent.labels[0].value))
outcome = ''
break
l += 1
sub_outcomes.append(tuple(sub_instances))
instances.clear()
data_to_classify = [' '.join(j[0] for j in i) for i in data_to_classify]
data_to_classify_frame = pd.DataFrame(data_to_classify,
columns=['Abstract'])
max_outcomes_per_sentence = max([len(i) for i in sub_outcomes])
columns_ = [
'Outcome {}'.format(i + 1) for i in range(max_outcomes_per_sentence)
]
sub_outcomes_frame = pd.DataFrame(sub_outcomes, columns=columns_)
data_to_classify_frame = pd.concat(
[data_to_classify_frame, sub_outcomes_frame], axis=1)
print(tabulate(data_to_classify_frame, headers='keys', tablefmt='psql'))
def clean(raw):
""" Remove hyperlinks and markup """
result = re.sub("<[a][^>]*>(.+?)</[a]>", 'Link.', raw)
result = re.sub('>', "", result)
result = re.sub(''', "'", result)
result = re.sub('"', '"', result)
result = re.sub('/', ' ', result)
result = re.sub('<p>', ' ', result)
result = re.sub('</i>', '', result)
result = re.sub('>', '', result)
result = re.sub('<i>', ' ', result)
result = re.sub("\n", '', result)
return result
classifier = TextClassifier.load('./model_result/final-model.pt')
#df = review_dataframe
#df = df.head(10)
df['text'] = df['text'].fillna('').apply(str)
d = []
for i, row in df.iterrows():
document = row['text']
document = clean(document)
sentence = Sentence(document)
classifier.predict(sentence)
print(document + "\n\n")
print(sentence.labels)
def get_classifier_model(model_name) -> TextClassifier:
return TextClassifier.load(model_name)
reproject_words_dimension=256,
)
# 5. create the text classifier
classifier = TextClassifier(document_embeddings, label_dictionary=label_dict)
# 6. initialize the text classifier trainer
trainer = ModelTrainer(classifier, corpus)
# 7. start the training
trainer.train('/home/anna/Desktop/markup/8',
learning_rate=0.1,
mini_batch_size=32,
anneal_factor=0.5,
patience=5,
max_epochs=150)
# 8. plot weight traces (optional)
from flair.visual.training_curves import Plotter
plotter = Plotter()
plotter.plot_weights('/home/anna/Desktop/markup/8/weights.txt')
classifier = TextClassifier.load('/home/anna/Desktop/markup/8/final-model.pt')
# create example sentences
sentence = Sentence('France is the current world cup winner.')
# predict class and print
classifier.predict(sentence)
print(sentence.labels)
def load_flair():
return TextClassifier.load('en-sentiment')
def __init__(self, model_name_or_path: str):
self.classifier = TextClassifier.load(model_name_or_path)
# Create model
from flair.models import TextClassifier
classifier = TextClassifier(document_embeddings, label_dictionary=label_dict)
# Create model trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(classifier, corpus)
# Train the model
trainer.train('model-saves',
learning_rate=0.1,
mini_batch_size=32,
anneal_factor=0.5,
patience=8,
max_epochs=200)
# Load the model and make predictions
from flair.data import Sentence
classifier = TextClassifier.load('model-saves/final-model.pt')
pos_sentence = Sentence(preprocess('I love Python!'))
neg_sentence = Sentence(preprocess('Python is the worst!'))
classifier.predict(pos_sentence)
classifier.predict(neg_sentence)
print(pos_sentence.labels, neg_sentence.labels)
def __init__(self, path_to_model: str) -> None:
"Input Flair trained sentiment model"
from flair.models import TextClassifier
self.classifier = TextClassifier.load(path_to_model)
def test_train_load_use_classifier_multi_label(results_base_path,
tasks_base_path):
corpus = flair.datasets.ClassificationCorpus(tasks_base_path /
"multi_class",
label_type="topic")
label_dict = corpus.make_label_dictionary(label_type="topic")
model: TextClassifier = TextClassifier(
document_embeddings=document_embeddings,
label_dictionary=label_dict,
label_type="topic",
multi_label=True,
)
trainer = ModelTrainer(model, corpus)
trainer.train(
results_base_path,
mini_batch_size=1,
max_epochs=20,
shuffle=False,
checkpoint=False,
train_with_test=True,
train_with_dev=True,
)
sentence = Sentence("apple tv")
model.predict(sentence)
assert "apple" in [label.value for label in sentence.labels]
assert "tv" in [label.value for label in sentence.labels]
for label in sentence.labels:
print(label)
assert label.value is not None
assert 0.0 <= label.score <= 1.0
assert type(label.score) is float
del trainer, model, corpus
loaded_model = TextClassifier.load(results_base_path / "final-model.pt")
sentence = Sentence("apple tv")
loaded_model.predict(sentence)
assert "apple" in [label.value for label in sentence.labels]
assert "tv" in [label.value for label in sentence.labels]
for label in sentence.labels:
assert label.value is not None
assert 0.0 <= label.score <= 1.0
assert type(label.score) is float
sentence = Sentence("I love Berlin")
sentence_empty = Sentence(" ")
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
del loaded_model
from flask import Flask, request, jsonify
from flask_cors import CORS
from flair.models import TextClassifier
from flair.data import Sentence
classifier = TextClassifier.load('./model/best-model.pt')
mapping = {
'sad': '😞',
'smile': '😀',
'food': '🍽',
'heart': '❤',
'baseball': '⚾'
}
app = Flask(__name__)
CORS(app)
@app.route('/emojify', methods=['POST'])
def emoji():
data = request.form.get('text')
if not len(data.strip()):
return ''
sentence = Sentence(data)
classifier.predict(sentence)
print(str(sentence.labels))
if 'sad' in str(sentence.labels):
return mapping['sad']
elif 'smile' in str(sentence.labels):
return mapping['smile']
elif 'food' in str(sentence.labels):
return mapping['food']
class SentimentAnalysisAPI(object):
default_sentence: str = "N/A"
flair_sent_model: TextClassifier = TextClassifier.load("sentiment")
nltk_sent_model: SentimentIntensityAnalyzer = SentimentIntensityAnalyzer()
@staticmethod
def get_document_language(doc: str) -> str:
language: str = ""
try:
language: str = detect(doc)
except Exception as e:
logger.error(e)
return language
@staticmethod
def translate_to_english(src_doc: str, src_lang: str) -> str:
translation: str = ""
try:
eng_translator: Translator = Translator()
res: Translated = eng_translator.translate(src_doc,
src=src_lang,
dest="en")
translation: str = res.text
except Exception as e:
logger.error(e)
return translation
@staticmethod
def make_sentences(text: str, min_char: int = 3) -> list:
""" Break apart text into a list of sentences """
if len(text) > min_char:
sentences: list = [
sent for sent in split_single(text) if len(sent) > min_char
]
else:
sentences: list = []
if not sentences:
logger.warning("Default sentence was added")
sentences: list = [SentimentAnalysisAPI.default_sentence]
return sentences
@staticmethod
def get_label_decision(score: float,
lower_boundary: float = .4,
upper_boundary: float = .6) -> str:
final_label: str = ""
try:
if score < lower_boundary:
final_label: str = "Negative"
elif lower_boundary <= score < upper_boundary:
final_label: str = "Neutral"
else:
final_label: str = "Positive"
except Exception as e:
logger.error(e)
return final_label
@staticmethod
def get_flair_sentiment_analysis(doc: str) -> FlairSentOutput:
output: FlairSentOutput = FlairSentOutput()
try:
was_translated: bool = False
# 1. Get the language of the document
lang: str = SentimentAnalysisAPI.get_document_language(doc=doc)
if lang != "en":
# Translate
doc: str = SentimentAnalysisAPI.translate_to_english(
src_doc=doc, src_lang=lang)
was_translated: bool = True
sentences: list = SentimentAnalysisAPI.make_sentences(text=doc)
scores: list = []
for sent in sentences:
# 2. Load model and predict
sentence: Sentence = Sentence(sent)
SentimentAnalysisAPI.flair_sent_model.predict(sentence)
single_res: Label = sentence.labels[0]
single_label: str = single_res.value
single_score: float = single_res.score if single_label == "POSITIVE" else (
1 - single_res.score)
scores.append(single_score)
final_score: float = round(float(np.mean(scores)), 3)
final_label: str = SentimentAnalysisAPI.get_label_decision(
score=final_score)
output: FlairSentOutput = FlairSentOutput(
label=final_label,
confidence=final_score,
translated=was_translated,
analysed=True)
except Exception as e:
logger.error(e)
return output
@staticmethod
def get_textblob_sentiment_analysis(doc: str) -> TextBlobSentOutput:
output: TextBlobSentOutput = TextBlobSentOutput()
try:
was_translated: bool = False
# 1. Get the language of the document
lang: str = SentimentAnalysisAPI.get_document_language(doc=doc)
if lang != "en":
# Translate
doc: str = SentimentAnalysisAPI.translate_to_english(
src_doc=doc, src_lang=lang)
was_translated: bool = True
sentences: list = SentimentAnalysisAPI.make_sentences(text=doc)
polarity_scores: list = []
subjectivity_scores: list = []
for sent in sentences:
subjectivity: float = TextBlob(sent).sentiment.subjectivity
polarity: float = TextBlob(sent).sentiment.polarity
polarity_scores.append(polarity)
subjectivity_scores.append(subjectivity)
final_subjectivity: float = round(
float(np.mean(subjectivity_scores)), 3)
final_polarity: float = round(float(np.mean(polarity_scores)), 3)
output: TextBlobSentOutput = TextBlobSentOutput(
analysed=True,
polarity=final_polarity,
subjectivity=final_subjectivity,
translated=was_translated)
except Exception as e:
logger.error(e)
return output
@staticmethod
def get_nltk_sentiment_analysis(doc: str) -> NLTKSentOutput:
output: NLTKSentOutput = NLTKSentOutput()
try:
was_translated: bool = False
# 1. Get the language of the document
lang: str = SentimentAnalysisAPI.get_document_language(doc=doc)
if lang != "en":
# Translate
doc: str = SentimentAnalysisAPI.translate_to_english(
src_doc=doc, src_lang=lang)
was_translated: bool = True
sentences: list = SentimentAnalysisAPI.make_sentences(text=doc)
polarity_scores: list = []
for sent in sentences:
polarity_scores.append(
SentimentAnalysisAPI.nltk_sent_model.polarity_scores(
text=sent))
neg_prob: float = SentimentAnalysisAPI.get_nltk_scores(
scores=polarity_scores, key="neg")
neu_prob: float = SentimentAnalysisAPI.get_nltk_scores(
scores=polarity_scores, key="neu")
pos_prob: float = SentimentAnalysisAPI.get_nltk_scores(
scores=polarity_scores, key="pos")
compound_prob: float = SentimentAnalysisAPI.get_nltk_scores(
scores=polarity_scores, key="compound")
output: NLTKSentOutput = NLTKSentOutput(
analysed=True,
negative_prob=neg_prob,
neutral_prob=neu_prob,
positive_prob=pos_prob,
compound_prob=compound_prob,
translated=was_translated)
except Exception as e:
logger.error(e)
return output
@staticmethod
def get_nltk_scores(scores: list, key: str) -> float:
score: float = 0.0
try:
score: float = round(float(np.mean([i.get(key) for i in scores])),
3)
except Exception as e:
logger.error(e)
return score
def get_lyrics_df(folder, artist):
lyrics_df = concat_lyrics_df(folder, artist)
lyrics_df.loc[:, 'artist'] = artist
# Create columns
lyrics_df.loc[:, 'artist_wrote_song'] = lyrics_df.apply(
lambda x: 1 if x['artist'] in x['writers'] else 0, axis=1)
lyrics_df.loc[:, 'artist_produced_song'] = lyrics_df.apply(
lambda x: 1 if x['artist'] in x['producers'] else 0, axis=1)
lyrics_df.loc[:, 'structure_tags'] = lyrics_df['lyrics'].apply(
lambda x: re.findall(r"(\[.*\])", x))
lyrics_df.loc[:, 'lyrics_clean'] = lyrics_df.apply(
lambda x: clean_lyrics(x['lyrics'], x['structure_tags']), axis=1)
lyrics_df.loc[:,
'structure_tags_clean'] = lyrics_df['structure_tags'].apply(
lambda x: clean_structure_tags(x))
lyrics_df.loc[:,
'song_structure'] = lyrics_df['structure_tags_clean'].apply(
lambda x: get_song_structure(x))
# Analyze tokens and text
nlp = spacy.load('en', disable=['parser', 'ner'])
lyrics_df.loc[:, 'total_word_count'] = lyrics_df['lyrics_clean'].apply(
lambda x: len(nlp(x)))
lyrics_df.loc[:, 'lyrics_lemmatized'] = lyrics_df['lyrics_clean'].apply(
lambda x: ' '.join([
token.lemma_.lower() for token in nlp(x)
if token.is_alpha and not token.is_stop
]))
lyrics_df['lemma_count'] = lyrics_df['lyrics_lemmatized'].apply(
lambda x: len(x.split(' ')))
lyrics_df['unique_lemmas_on_song'] = lyrics_df['lyrics_lemmatized'].apply(
lambda x: len(set(x.split(' '))))
# Sentiment analysis
classifier = TextClassifier.load('sentiment')
sents = []
for i in range(0, len(lyrics_df.index.tolist())):
text = lyrics_df.iloc[i]['lyrics_clean']
if text:
sentence = Sentence(text)
classifier.predict(sentence)
sents.append(str(sentence.labels))
else:
sents.append('')
lyrics_df.loc[:, 'flair_sentiment'] = sents
lyrics_df.loc[:, 'sentiment_label'] = lyrics_df['flair_sentiment'].str[
1:-1].str.split('(', expand=True).iloc[:, 0].fillna('').str.strip()
lyrics_df.loc[:,
'sentiment_probability'] = lyrics_df['flair_sentiment'].str[
1:-1].str.split('(',
expand=True).iloc[:,
1].str[:-1].fillna(0)
lyrics_df.loc[:, 'sentiment'] = lyrics_df['sentiment_label'].apply(
lambda x: 1 if x == 'POSITIVE' else -1 if x == 'NEGATIVE' else 0)
lyrics_df.loc[:, 'sentiment_score'] = (
lyrics_df['sentiment_probability'].astype(float) - 0.5)
lyrics_df.loc[:, 'sentiment_score'] = lyrics_df[
'sentiment_score'] * lyrics_df['sentiment']
os.chdir(folder / '{}'.format(artist))
lyrics_df.to_csv(
'lyrics_final.csv',
index=False,
)
return lyrics_df
from pymongo import MongoClient
import pandas as pd
import numpy as np
from flair.models import TextClassifier
from flair.data import Sentence
import logging
logging.basicConfig(level=logging.ERROR)
###### Mongodb connection
client = MongoClient('localhost', 27017)
db = client.covML
data_col = db.scrapedData
############################
flair_sentiment = TextClassifier.load('en-sentiment')
##
def analyze_sentiment(headline):
s = Sentence(headline)
flair_sentiment.predict(s)
total_sentiment = s.labels[0].to_dict()
return total_sentiment
##
def analyseSentiments():
myNews=data_col.find()
df = pd.DataFrame(myNews)
del df['_id']
df['Result'] = np.array([analyze_sentiment(headline)['value'] for headline in df['headline']])
df['confidence'] = np.array([analyze_sentiment(headline)['confidence'] for headline in df['headline']])
data_col.delete_many({})
