<\/p>\n
1. Preparation<\/strong> Given that data is saved as a text file, you need to convert text into an object. Also, import TextBlob.<\/p>\n Read data from the file and create the new file which will include the result of text processing.<\/p>\n Convert every line of f1 file to an object to interpret textual data. Also, only English sentences are set to be analyzed.<\/p>\n Prior to applying TextBlob to objects, you might need to change or sort them out. Also, you have to define \u201cKeyword\u201d that you want to analyze before saving output. As you can see the following codes, the output of sentiment analysis consists of \u201cKeyword\u201d, polarity, and subjectivity of each tweet.<\/p>\n Now, you can see a new csv file in the directory that you designated like the one below. This will be used as the fundamental data for analysis in R.<\/p>\n
\nIn order to analyze data, install the library as follows:
\n<\/code><\/p>\npip install -U textblob
\npython -m textblob.download_corpora<\/code><\/p>\n
\n2. TextBlob<\/strong><\/p>\nimport os
\nimport json
\nfrom textblob import TextBlob
\n<\/code><\/p>\nf1 = open(\"name of data file\",\"r\")
\nf2 = open(\u201cname of output file\u201d, \u201cw\u201d)
\n<\/code><\/p>\nfor line in f1
\nobj = json.loads(line)
\nif tweetobj['lang']!=\"en\":
\ncontinue
\ntweet = obj['text'].strip()
\n<\/code><\/p>\ntweetBlob = TextBlob(tweet)
\nout = Keyword+\",\"+str(tweetBlob.sentiment.polarity)+\",\"+str(tweetBlob.sentiment.subjectivity)
\nfp2.write(out+\"\\n\")
\nfp1.close()
\nfp2.close()
\n<\/code><\/p>\n
![\"output\"](\"http:\/\/jkthinks.synology.me\/wp-content\/uploads\/2015\/01\/output-1024x742.jpg\")
<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"