somya-core-nlp

//installing core nlp and running it on port 9000
wget http://nlp.stanford.edu/software/stanford-corenlp-full-2017-06-09.zip
unzip stanford-corenlp-full-2017-06-09.zip
cd stanford-corenlp-full-2017-06-09
java -mx5g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -timeout 10000

// install python module for nlp
pip install pycorenlp

//python code starts here
import requests
from pycorenlp import StanfordCoreNLP
nlp = StanfordCoreNLP('http://localhost:9000')
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
p_url="https://www.amazon.in/gp/product/9311122521/ref=s9u_ri_gw_i1?ie=UTF8&pd_rd_i=9311122521&pd_rd_r=KDGDTZQ3BHQ7DSQZ0Z60&pd_rd_w=uU6yd&pd_rd_wg=KoSrl&pf_rd_m=A1VBAL9TL5WCBF&pf_rd_s=&pf_rd_r=F1YP5DR0PCJS3DS99C2Z&pf_rd_t=36701&pf_rd_p=3c777619-829a-489c-8bef-17dc6cebf439&pf_rd_i=desktop"
from bs4 import BeautifulSoup
response=requests.get(p_url,headers=headers)
print response.status_code
soup=BeautifulSoup(response.content,"html5lib")
a=soup.find_all("div",{"class":"a-expander-content"})

def do_stuff(data_string):
	res=nlp.annotate(data_string,
                   properties={
                       'annotators': 'sentiment',
                       'outputFormat': 'json',
                       'timeout': 1000,
                   })
	for s in res["sentences"]:
	    print "%d: '%s': %s %s" % (
	        s["index"],
	        " ".join([t["word"] for t in s["tokens"]]),
	        s["sentimentValue"], s["sentiment"])
	return True

for i in a:
	try:
		comment_string=str(i.getText())
		# print comment_string
		# print type(comment_string),"text_type"
		do_stuff(comment_string)
	except:
		pass