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c#sentiment-analysis
Weezel Tan
Weezel Tan

Reputation: 65

Facebook Sentiment Analysis API

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 ;)

Upvotes: 3

Views: 3253

Answers (4)

infinity
infinity

Reputation: 1920

If you are looking for an Open Source implementation of sentiment analysis engine based on Naive Bayes classifier in C#, take a peek at https://github.com/amrishdeep/Dragon. It works best on large corpus of words like blog posts or multi-paragraph product reviews. However, I am not sure if it would work for facebook posts that have a handful of words

Upvotes: 0

vpekar
vpekar

Reputation: 3355

There are lots of sentiment analysis APIs that you can easily incorporate into your app, also many have a free usage allowance (usually, 500 requests a day). I started a small project that compares how each API (currently supporting 10 different APIs: AIApplied, Alchemy, Bitext, Chatterbox, Datumbox, Lymbix, Repustate, Semantria, Skyttle, and Viralheat) classifies a given set of texts into positive, negative or neutral: https://github.com/skyttle/sentiment-evaluation

Each specific API can offer lots of other features, like classifying emotions (delight, anger, sadness, etc) or linking sentiment to entities the sentiment is attributed to. You just need to go through available features and pick the one that suits your needs.

TextBlob is another possiblity, though it will only classify texts into pos/neg/neu.

Upvotes: 1

Steve L
Steve L

Reputation: 1714

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)

Upvotes: 3

Jared
Jared

Reputation: 521

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.

Upvotes: 1

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