import pandas as pd
import numpy as np
df = pd.read_csv("./email1.csv")
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report
X = df['emails']
tfids = TfidfVectorizer(max_features=10000,ngram_range=(1,2))
X = tfids.fit_transform(X)
y = df['sentiment']
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)
clf = LinearSVC()
clf.fit(X_train,y_train)
y_pred = clf.predict(X_test)
print(classification_report(y_test,y_pred))
x = """The patient presents to clinic for initial evaluation of some pain and swelling to the left foot. On the day of injury a metal bar fell directly on the top of his left foot striking above the steel toed area of his boot. He had some pain, swelling and bruising early on but this has gotten better. However, the patient continues to have some soreness on the ball of his foot and points to the seconcbdista: metatarsal region. He states that it feels a little like a stone bruise. He has continued hs regglar duties without problems and is able to wear a regular shoe."""
vec = tfids.transform([x])
a = clf.predict(vec)
if a==0:
print("Negative communication")
else:
print("Positive communication")
有什么方法可以識別每個句子的情緒。例如：“他早期有一些疼痛、腫脹和瘀傷，但現在已經好轉了”，這是一個積極的句子。
uj5u.com熱心網友回復：
如果我理解正確，您只需將段落拆分為點，例如：
for sentence in x.split("."):
vec = tfids.transform([sentence])
a = clf.predict(vec)
print(f'Sentiment analysis for "{sentence}":')
if a==0:
print("Negative communication")
else:
print("Positive communication")
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