Question

我有一个大型的csv，其中包含来自博客的数千条评论，我想对使用textblob和nltk进行情感分析。

我正在使用https://wafawaheedas.gitbooks.io/twitter-sentiment-analysis-visualization-tutorial/sentiment-analysis-using-textblob.html中的python脚本，但已针对Python3进行了修改。

'''
uses TextBlob to obtain sentiment for unique tweets
'''

from importlib import reload
import csv
from textblob import TextBlob
import sys

# to force utf-8 encoding on entire program
#sys.setdefaultencoding('utf8')

alltweets = csv.reader(open("/path/to/file.csv", 'r', encoding="utf8", newline=''))
sntTweets = csv.writer(open("/path/to/outputfile.csv", "w", newline=''))

for row in alltweets:
    blob = TextBlob(row[2])
    print (blob.sentiment.polarity)
    if blob.sentiment.polarity > 0:
        sntTweets.writerow([row[0], row[1], row[2], row[3], blob.sentiment.polarity, "positive"])
    elif blob.sentiment.polarity < 0:
        sntTweets.writerow([row[0], row[1], row[2], row[3], blob.sentiment.polarity, "negative"])
    elif blob.sentment.polarity == 0.0:
        sntTweets.writerow([row[0], row[1], row[2], row[3], blob.sentiment.polarity, "neutral"])

但是，当我运行它时，我不断得到

    $ python3 sentiment.py
Traceback (most recent call last):
  File "sentiment.py", line 17, in <module>
    blob = TextBlob(row[2])
IndexError: list index out of range

我知道错误的含义，但是我不确定该怎么做。

对我想念的东西有什么想法吗？谢谢！

Answer 1

玩了一会儿之后，我想出了一个使用熊猫的更优雅的解决方案

from textblob import TextBlob
import pandas as pd

df = pd.read_csv("pathtoinput.csv", na_values='', 
encoding='utf8',keep_default_na=False, low_memory=False)

columns = ['text']

df = df[columns]

df['tweet'] = df['text'].astype('str')

df['polarity'] = df['tweet'].apply(lambda tweet: 
TextBlob(tweet).sentiment.polarity)

df.loc[df.polarity > 0, 'sentiment'] ='positive'
df.loc[df.polarity == 0, 'sentiment'] ='neutral'
df.loc[df.polarity < 0, 'sentiment'] ='negative'

df.to_csv("pathtooutput.csv", encoding='utf-8', index=False)

TextBlob到csv

1 个答案: