simple nltk sentiment analysis code using python3

Question

I am trying to do some classification on customer emails.

1. Is the email happy or sad (sentiment analysis)
2. Is the email related to billing or not.

I am using Python3 and think I have to use nltk and scikit NLTK - will help understand and read the text I beleive scikit - will do the classification (happy, sad and billing or not)

Training data set 1: A few phrases...anywhere from one word to a sentence with 5 to 6 words. (1 being happy and 0 being not happy)...a few examples below

• Apprecaite the help..1
• great job..1
• Awesome..1
• terrible..0
• confusing...0
• slow down...0

Training data set 2: a few phrases indicating billing related question..(few examples below)

• question on my bill
• billing fee
• my bill is too high
• payment rejected

Now this seems to be straight forward from a concept stand point where can I find some basic code, that will tell me

1. how I can use my own training data
2. how I can load the email text as input and spit out an answer happy or sad...and billing or not.

Answer 1

Regarding your data sets, your approach is nearly lexicon-based as the items contains very few words.

For billing, the lexicon-based approach should be a good idea. You should give importance to the subjects of the emails.

For sentiment analysis you have two options:

• Machine learning: In this case you should use a bigger data set (in my view, each item should be a full email). You can implement a Naive Bayes classifier following this tutorial.

• Lexicon-based approach: There are several lexicons for sentiment analysis e.g. SentiWordNet (downloadable from nltk.download()), MPQA, SentiStrength, WordNet-Affect via WNAffect,... Preprocessings: tokenization (nltk.word_tokenize()) and POS tagging (nltk.pos_tag(text)). You should also think about negation (polarity shifting is a good approach to manage with negation).

Machine Learning provide best results so if you have enough annotated emails it is the good choice.