1.爬取股票评论和对应数据 2.对股票评论使用jieba库进行分词 3.对分词后的文本进行特征化提取 4.使用已经分词的积极和消极文本对svm，knn，逻辑回归，决策树，贝叶斯，随机森林，adaboost等方法进行分词结果评测 5.得到svm结果最优，使用svm对已处理好样本进行训练得到模型 6.使用模型对评论进行划分，得到评论是否积极或者消极 7.根据对所有评论划分结果使用 bi=ln（1+pos）/（1+neg）指数来表示不同评价对每天的影响。 8.将bi的指数放大窗口10倍，来评价情绪对股票的影响
import os from time import time import pandas as pd import numpy as np import pickle from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer from sklearn.model_selection import train_test_split, cross_val_score, KFold from sklearn.feature_selection import SelectKBest, chi2 from sklearn.utils.extmath import density from sklearn import svm from sklearn import naive_bayes from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression, SGDClassifier from sklearn.ensemble import AdaBoostClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn import metrics from sklearn.utils import shuffle np.random.seed(42) comment_file = './data/stock_comments_seg.csv' data_path = './data' pos_corpus = 'positive.txt' neg_corpus = 'negative.txt' K_Best_Features = 3000 def load_dataset(): pos_file = os.path.join(data_path, pos_corpus) neg_file = os.path.join(data_path, neg_corpus) pos_sents = [] with open(pos_file, 'r', encoding='utf-8') as f: for sent in f: pos_sents.append(sent.replace('\n', '')) neg_sents = [] with open(neg_file, 'r', encoding='utf-8') as f: for sent in f: neg_sents.append(sent.replace('\n', '')) balance_len = min(len(pos_sents), len(neg_sents)) pos_df = pd.DataFrame(pos_sents, columns=['text']) pos_df['polarity'] = 1 pos_df = pos_df[:balance_len] neg_df = pd.DataFrame(neg_sents, columns=['text']) neg_df['polarity'] = 0 neg_df = neg_df[:balance_len] return pd.concat([pos_df, neg_df]).reset_index(drop=True) # return pd.concat([pos_df, neg_df]).sample(frac=1).reset_index(drop=True) def load_dataset_tokenized(): pos_file = os.path.join(data_path, pos_corpus) neg_file = os.path.join(data_path, neg_corpus) pos_sents = [] with open(pos_file, 'r', encoding='utf-8') as f: for line in f: tokens = line.split(' ') sent = [] for t in tokens: if t.strip(): sent.append(t.strip()) pos_sents.append(sent) neg_sents = [] with open(neg_file, 'r', encoding='utf-8') as f: for line in f: tokens = line.split(' ') sent = [] for t in tokens: if t.strip(): sent.append(t.strip()) neg_sents.append(sent) balance_len = min(len(pos_sents), len(neg_sents)) texts = pos_sents + neg_sents labels = [1] * balance_len + [0] * balance_len return texts, labels def KFold_validation(clf, X, y): acc = [] pos_precision, pos_recall, pos_f1_score = [], [], [] neg_precision, neg_recall, neg_f1_score = [], [], [] kf = KFold(n_splits=5, shuffle=True, random_state=42) for train, test in kf.split(X): X_train = [X[i] for i in train] X_test = [X[i] for i in test] y_train = [y[i] for i in train] y_test = [y[i] for i in test] # vectorizer = TfidfVectorizer(analyzer='word', tokenizer=lambda x : (w for w in x.split(' ') if w.strip())) def dummy_fun(doc): return doc vectorizer = TfidfVectorizer(analyzer='word', tokenizer=dummy_fun, preprocessor=dummy_fun, token_pattern=None) vectorizer.fit(X_train) X_train = vectorizer.transform(X_train) X_test = vectorizer.transform(X_test) clf.fit(X_train, y_train) preds = clf.predict(X_test) acc.append(metrics.accuracy_score(y_test, preds)) pos_precision.append(metrics.precision_score(y_test, preds, pos_label=1)) pos_recall.append(metrics.recall_score(y_test, preds, pos_label=1)) pos_f1_score.append(metrics.f1_score(y_test, preds, pos_label=1)) neg_precision.append(metrics.precision_score(y_test, preds, pos_label=0)) neg_recall.append(metrics.recall_score(y_test, preds, pos_label=0)) neg_f1_score.append(metrics.f1_score(y_test, preds, pos_label=0)) return (np.mean(acc), np.mean(pos_precision), np.mean(pos_recall), np.mean(pos_f1_score), np.mean(neg_precision), np.mean(neg_recall), np.mean(neg_f1_score)) def benchmark_clfs(): print('Loading dataset...') X, y = load_dataset_tokenized() classifiers = [ ('LinearSVC', svm.LinearSVC()), ('LogisticReg', LogisticRegression()), ('' '' '', SGDClassifier()), ('MultinomialNB', naive_bayes.MultinomialNB()), ('KNN', KNeighborsClassifier()), ('DecisionTree', DecisionTreeClassifier()), ('RandomForest', RandomForestClassifier()), ('AdaBoost', AdaBoostClassifier(base_estimator=LogisticRegression())) ] cols = ['metrics', 'accuracy', 'pos_precision', 'pos_recall', 'pos_f1_score', 'neg_precision', 'neg_recall', 'neg_f1_score'] scores = [] for name, clf in classifiers: score = KFold_validation(clf, X, y) row = [name] row.extend(score) scores.append(row) df = pd.DataFrame(scores, columns=cols).T df.columns = df.iloc[0] df.drop(df.index[[0]], inplace=True) df = df.apply(pd.to_numeric, errors='ignore') return df def dummy_fun(doc): return doc def eval_model(): print('Loading dataset...') X, y = load_dataset_tokenized() clf = svm.LinearSVC() vectorizer = TfidfVectorizer(analyzer='word', tokenizer=dummy_fun, preprocessor=dummy_fun, token_pattern=None) X = vectorizer.fit_transform(X) print('Train model...') clf.fit(X, y) print('Loading comments...') df = pd.read_csv(comment_file) df.dropna(inplace=True) df.reset_index(drop=True, inplace=True) df['created_time'] = pd.to_datetime(df['created_time'], format='%Y-%m-%d %H:%M:%S') df['polarity'] = 0 df['title'].apply(lambda x: [w.strip() for w in x.split()]) texts = df['title'] texts = vectorizer.transform(texts) preds = clf.predict(texts) df['polarity'] = preds df.to_csv('stock_comments_analyzed.csv', index=False) if __name__ == '__main__': scores = benchmark_clfs() print(scores) scores.to_csv('model_ml_scores.csv', float_format='%.4f') eval_model()