Facebook Sentiment Analysis API

Question

i want to try and create an application which rates the user's facebook posts based on the content (Sentiment Analysis). I tried creating an algorithm myself initially but i felt it wasn't that reliable. Created a dictionary list of words and scanned the posts against the dictionary and rate if it was positive or negative. However, i feel this is minimal. I would like to rate the mood or feelings/personality traits of the person based on the posts. Is this possible to be done?

Would hope to make use of some online APIs, please assist. Thanks ;)

Answer 1

As @Jared pointed out, using a dictionary-based approach can work quite well in some situations, depending on the quality of your training corpus. This is actually how CLIPS pattern and TextBlob's implementations work.

Here's an example using TextBlob:

from text.blob import TextBlob
b = TextBlob("StackOverflow is very useful")
b.sentiment  # returns (polarity, subjectivity)
# (0.39, 0.0)

By default, TextBlob uses pattern's dictionary-based algorithm. However, you can easily swap out algorithms. You can, for example, use a Naive Bayes classifier trained on a movie reviews corpus.

from text.blob import TextBlob
from text.sentiments import NaiveBayesAnalyzer

b = TextBlob("Today is a good day", analyzer=NaiveBayesAnalyzer())
b.sentiment  # returns (label, prob_pos, prob_neg)
# ('pos', 0.7265237431528468, 0.2734762568471531)

Answer 2

The algorithm you describe should actually work well, but the quality of the result depends greatly on the word list used. For Sentimental, we take comments on Facebook posts and score them based on sentiment. Using the AFINN 111 word list to score the comments word by word, this approach is (perhaps surprisingly) effective. By normalizing and stemming the words first, you should be able to do even better.