анализ настроений с использованием отдельных наборов данных - PullRequest
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анализ настроений с использованием отдельных наборов данных

0 голосов
/ 22 апреля 2019

Я использовал следующий код, чтобы найти точность алгоритмов машинного обучения, разделив мой тренировочный набор данных. У меня есть отдельный набор данных под названием testing.csv, содержащий только твиты, и я хотел бы предсказать настроение с помощью алгоритмов машинного обучения и training500.csv. Как это возможно сделать? 

import pandas as pd
df = pd.read_csv('training500.csv')
df.head()

df = df[pd.notnull(df['Text'])]
df.info()
col = ['Text', 'Sentiment']
df = df[col]
df.columns
df.columns = ['Text', 'Sentiment']
df['category_id'] = df['Sentiment'].factorize()[0]
from io import StringIO
category_id_df = df[['Sentiment',       'category_id']].drop_duplicates().sort_values('category_id')
category_to_id = dict(category_id_df.values)
id_to_category = dict(category_id_df[['category_id',   'Sentiment']].values)
df.head()
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8,6))
df.groupby('Sentiment').Text.count().plot.bar(ylim=0)
plt.show()
from sklearn.feature_extraction.text import TfidfVectorizer

tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2',      encoding='latin-1', ngram_range=(1, 2), stop_words='english')

features = tfidf.fit_transform(df.Text).toarray()
labels = df.category_id
features.shape

from sklearn.feature_selection import chi2
import numpy as np

N = 2
for Sentiment, category_id in sorted(category_to_id.items()):             features_chi2 = chi2(features, labels == category_id)
  indices = np.argsort(features_chi2[0])
  feature_names = np.array(tfidf.get_feature_names())[indices]
  unigrams = [v for v in feature_names if len(v.split(' ')) == 1]
  bigrams = [v for v in feature_names if len(v.split(' '))) == 2]
  print("# '{}':".format(Sentiment))
  print("  . Most correlated unigrams:\n       . {}".format('\n       . '.join(unigrams[-N:])))
  print("  . Most correlated bigrams:\n       . {}".format('\n       . '.join(bigrams[-N:])))


from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB

X_train, X_test, y_train, y_test = train_test_split(df['Text'], df['Sentiment'], random_state = 0)
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)


from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC

from sklearn.model_selection import cross_val_score


models = [
    RandomForestClassifier(n_estimators=200, max_depth=3,     random_state=0),
    LinearSVC(),
    MultinomialNB(),
    LogisticRegression(random_state=0),
]
CV = 5
cv_df = pd.DataFrame(index=range(CV * lenstr((models)))
entries = []
for model in models:
  model_name = model.__class__.__name__
  accuracies = cross_val_score(model, features, labels,  scoring='accuracy', cv=CV)
  for fold_idx, accuracy in enumerate(accuracies):
    entries.append((model_name, fold_idx, accuracy))
cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])
import seaborn as sns

sns.boxplot(x='model_name', y='accuracy', data=cv_df)
sns.stripplot(x='model_name', y='accuracy', data=cv_df, 
          size=8, jitter=True, edgecolor="gray", linewidth=2)
plt.show()


cv_df.groupby('model_name').accuracy.mean()

model = LinearSVC()
X_train, X_test, y_train, y_test, indices_train, indices_test =    train_test_split(features, labels, df.index, test_size=0.33, random_state=0)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test, y_pred)
fig, ax = plt.subplots(figsize=(10,10))
sns.heatmap(conf_mat, annot=True, fmt='d',
        xticklabels=category_id_df.Sentiment.values,      yticklabels=category_id_df.Sentiment.values)
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.show()

from sklearn import metrics
print(metrics.classification_report(y_test, y_pred,     target_names=df['Sentiment'].unique()))
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