Sentiment analysis using python
Try to search from the databases of official &quot;bad words&quot; that google publishes in this link Google's official list of bad words.
Also, here is the link for the good words Not the official list of good words
For the code, I would do it like this:
textArray = file('dir_to_your_text','r').read().split()

#Bad words should be listed like this for the split function to work
# &quot;*** ****** **** ****&quot; the stars are for the cenzuration :P
badArray = file('dir_to_your_bad_word_file).read().split()
goodArray = file('dir_to_your_good_word_file).read().split()

# Then you use matching algorithm from difflib on good and bad word for every word in an array of words
import difflib

goodMachingCouter = 0;
badMacihngCouter = 0;


for iGood in range(0, len(goodArray)):
    for iWord in range(0, len(textArray)):
        goodMachingCounter += difflib.SequenceMatcher(None, goodArray[iGood], textArray[iWord]).ratio()
     
for iBad in range(0, len(badArray)):
    for iWord in range(0, len(textArray)):
        badMachingCounter += difflib.SequenceMatcher(None, badArray[ibad], textArray[iWgoodord]).ratio()

goodMachingCouter *= 100/(len(goodArray)*len(textArray))
badMacihngCouter *= 100/(len(badArray)*len(textArray))

print('Show the good measurment of the text in %: '+goodMachingCouter)
print('Show the bad measurment of the text in %: '+badMacihngCouter)
print('Show the hootnes of the text: ' + len(textArray)*goodMachingCounter)

The code will be slow but accurate :) I didn't run and test it please do it for me and post the correct code :) because I wanna test it too :)