[英]Sentiment analysis on Dataframe
I have a dataframe 'df' which has data like : 我有一个数据框“ df”,其数据如下:
State Text
0 California This is a beutiful day# It's too hard I am get...
1 Florida Can somebody please help me; I am new to python
2 New York But I am stuck with code How should I solve th...
This dataframe is created from a csv file using below code : 该数据帧是使用以下代码从csv文件创建的:
delimiter = ' '
df = df2.groupby('State')['Text'].apply(lambda x: "%s" % delimiter.join(x)).reset_index()
I need to do sentiment analysis(using TextBlob) on this dataframe 'df' state wise. 我需要对此数据框“ df”状态进行情感分析(使用TextBlob)。 Could anyone please help me to do the sentiment analysis state wise.
任何人都可以帮助我明智地进行情绪分析。 I tried to do it as:
我试图这样做:
for row in df.itertuples():
text = df.iloc[:, 1].tolist()
tweets = " ".join(str(x) for x in text)
text = TextBlob(tweets)
score = text.sentiment
But it gave me sentiment score of total dataframe, not sentiment score for each state separately 但是它给了我总数据帧的情感得分,而不是每个州的情感得分
My code gave output as : 我的代码给出的输出为:
Sentiment(polarity=-0.07765151515151517, subjectivity=0.49810606060606055)
But i want sentiment output for each row(that means for each state) separately. 但是我想分别为每一行输出情感(即针对每个状态)。
You can use apply()
in combination with a lambda
function. 您可以将
apply()
与lambda
函数结合使用。 This is a much more efficient way than looping. 这是比循环更有效的方法。
df[['polarity', 'subjectivity']] = df['Text'].apply(lambda Text: pd.Series(TextBlob(Text).sentiment))
This returns: 返回:
State Text polarity subjectivity
0 California This is a beutiful day# It's too hard I am get -0.291667 0.541667
1 Florida Can somebody please help me; I am new to python 0.136364 0.454545
2 New York But I am stuck with code How should I solve th 0.000000 0.000000
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