sentiwordnet scoring with python

Question

I have been working on a research in relation with twitter sentiment analysis. I have a little knowledge on how to code on Python. Since my research is related with coding, I have done some research on how to analyze sentiment using Python, and the below is how far I have come to: 1.Tokenization of tweets 2. POS tagging of token and the remaining is calculating Positive and Negative of the sentiment which the issue i am facing now and need your help.

Below is my code example:

import nltk
sentence = "Iphone6 camera is awesome for low light "
token = nltk.word_tokenize(sentence)
tagged = nltk.pos_tag(token)

Therefore, I want to ask if anybody can help me to show/guide the example of using python to code about sentiwordnet to calculate the positive and negative score of the tweeets that has already been POS tagged. thank in advance

Answer 1

It's a little unclear as to what exactly your question is. Do you need a guide to using Sentiwordnet? If so check out this link,

http://www.nltk.org/howto/sentiwordnet.html

Since you've already tokenized and POS tagged the words, all you need to do now is to use this syntax,

swn.senti_synset('breakdown.n.03')

Breaking down the argument,

• 'breakdown' = word you need scores for.
• 'n' = part of speech
• '03' = Usage (01 for most common usage and a higher number would indicate lesser common usages)

So for each tuple in your tagged array, create a string as above and pass it to the senti_synset function to get the positive, negative and objective score for that word.

Caveat: The POS tagger gives you a different tag than the one senti_synset accepts. Use the following to convert to synset notation.

n - NOUN 
v - VERB 
a - ADJECTIVE 
s - ADJECTIVE SATELLITE 
r - ADVERB 

(Credits to Using Sentiwordnet 3.0 for the above notation)

That being said, it is generally not a great idea to use Sentiwordnet for Twitter sentiment analysis and here's why,

Tweets are filled with typos and non-dictionary words which Sentiwordnet often times does not recognize. To counter this problem, either lemmatize/stem your tweets before you pos tag them or use a Machine Learning classifier such as Naive Bayes for which NLTK has built in functions. As for the training dataset for the classifier, either manually annotate a dataset or use a pre-labelled set such as, as the Sentiment140 corpus.

If you are uninterested in actually performing the sentiment analysis but need a sentiment tag for a given tweet, you can always use the Sentiment140 API for this purpose.