Comments on: Text Classification for Sentiment Analysis – Naive Bayes Classifier

By: Hiral

Hiral — Fri, 09 Mar 2012 14:19:00 +0000

hi i , i am working on sentiment analysis same positive and negative i want to use
naive bayes but i dnt know technique plz guide me from basic thank you

By: » A Text Analysis of Supreme Court Oral Arguments jarv.org

» A Text Analysis of Supreme Court Oral Arguments jarv.org — Sun, 05 Feb 2012 22:47:34 +0000

[...] For more information about sentiment analysis there is some good information here and in these two articles. Applying this to oral arguments? Well let’s leave it as just one way to look at [...]

By: Jacob Perkins

Jacob Perkins — Tue, 01 Nov 2011 15:05:00 +0000

The underlying model is an ensemble of binary NaiveBayes and MaximumEntropy classifiers, setup in a hierarchy of neutral-polar, then pos-neg. I’m sure there’s papers on those topics, but there’s nothing specific I referenced to create the API.

I don’t know of any API that does arousal in addition to valence. If you find one, please let me know.

By: jorrit

jorrit — Tue, 01 Nov 2011 11:53:00 +0000

Hi, i am about to use you API for my thesis. Could you perhaps point me to an published (scientifc ) article which explains the underlying model of your implementation?

Also: would you perhaps know about an API which returns sentiment as valence (pos vs neg) and arousal (calm vs activated)? I would like to do a comparison between the two types!

thanks alot and for all your work at the NLTK!

By: Utilizar NLTK desde IronPython 2.7 y Visual Studio « Sebastian Durandeu Blog

Thu, 15 Sep 2011 03:33:20 +0000

[...] Mas concretamente yo estoy utilizando NLTK desde IronPython para realizar análisis de sentimiento para saber si un texto sobre un tema en particular representa una opinión positiva o negativa. Hay algunos ejemplos en este otro artículo: http://streamhacker.com/2010/05/10/text-classification-sentiment-analysis-naive-bayes-classifier/ [...]

By: Jacob Perkins

Jacob Perkins — Fri, 19 Aug 2011 14:29:00 +0000

It all depends on what you want to classify. But whatever it is, you need a training corpus, ideally structured similarly to the movie_reviews corpus. Once you’ve got that, you can train a classifier in a very similar way.

By: Spin_maker

Spin_maker — Fri, 19 Aug 2011 09:11:00 +0000

Hi,
explaination is worth seeing , can you shed some light on how we can implement car evaluvation using navie bay’s classification algo

By: Jacob Perkins

Jacob Perkins — Mon, 11 Jul 2011 02:43:00 +0000

Yes, just pickle the trained classifier to a file, then reload/unpickle later. If you store the classifier in a nltk_data directory, you can also use nltk.data.load to load & unpickle the classifier.

By: Ritvik Mathur

Ritvik Mathur — Mon, 11 Jul 2011 02:26:00 +0000

Hi, Nice Explanation! I am working on a similar project and wanted to know if there is a way to save the trained model somehow and then be able to use/reload it later to classify news data that I input? Because right now every time I run the script it takes a long time to train the classifier since the training set is huge (300K samples).

By: Jacob Perkins

Jacob Perkins — Fri, 01 Jul 2011 20:11:00 +0000

Please see my followup article on this topic: http://streamhacker.com/2010/06/16/text-classification-sentiment-analysis-eliminate-low-information-features/

However, the words that are significant for movie reviews are likely to be different than words that are significant for tweets, so for best results, you will probably want your own corpus to learn from.