Note to readers: There have been many developments since I posted this article in 2012! I do plan to update the article.
I took a stab at a Quora question, What are the most powerful open-source sentiment-analysis tools?. Here’s my response:
I know of no open-source (software) tools dedicated to sentiment analysis. Instead, a variety of open-source text-analytics tools — natural-language processing for information extraction and classification — can be applied for sentiment analysis. They include —
– Python NLTK (Natural Language Toolkit), http://www.nltk.org/, but see also http://text-processing.com/demo/sentiment/
– R, TM (text mining) module, http://cran.r-project.org/web/packages/tm/index.html, including tm.plugin.sentiment.
– RapidMiner, http://rapid-i.com/content/view/184/196/.
– GATE, the General Architecture for Text Engineering, http://gate.ac.uk/sentiment/.
I’m sure you can also find UIMA-plug-in annotators for sentiment — Apache UIMA is the Unstructured Information Management Architecture, http://uima.apache.org/ — also sentiment classifiers for the WEKA data-mining workbench, http://www.cs.waikato.ac.nz/ml/weka/. See http://www.unal.edu.co/diracad/einternacional/Weka.pdf for one example.
I bet someone’s doing sentiment with the Stanford NLP tools, http://www-nlp.stanford.edu/software/, although my understanding is the maximum-entropy classification isn’t the best approach for sentiment. I’m no scientist so I won’t go into this.
Then there’s LingPipe, which can be characterized as pseudo-open source. See http://alias-i.com/lingpipe/demos/tutorial/sentiment/read-me.html.
Powerful, I can’t say. Where machine learning is involved, a lot will depend on your training set.
Note that the tools above work on textual sources. There may be open-source tools out there for information extraction from non-textual, sentiment-bearing sources such as speech (with the outputs fed into a classification engine such as some fo the above), but I haven’t looked into them. If you know of any, or have additions for my list above, please send me a note (grimes(at)altaplana.com).
I use the R CRAN library package “textir” in addition to “tm.sentiment”. “textir” also has a topic modeling routine now; see http://arxiv.org/abs/1012.2098 and http://arxiv.org/abs/1109.4518 for the math.
Thanks for the nice summary, Seth! The Apache Mahout library includes a Latent Dirichlet Allocation algorithm that can be used for topic identification, and might be used as part of a sentiment analysis process.
Forgot the url for Mahout: http://mahout.apache.org/
For an example of Sentiment Classification using RapidMiner and Supervised Learning, see:
http://tinyurl.com/colsjhu
Anyone has used any opensource tool for sentiment analysis of Arabic text ? for twitter data and web articles?
Hi Karimkhanp maybe it’s too late to answer your question but it will be usefull for others who asks the same question; Actually there is some tools, I know two of them and tested only one, there is Tashaphyne, and ISRI python packages you can find documentation for the two packages.
What is your review of MeTA langiage? there is a course in coursera about information retrieval which uses this language. Thanks!