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 —
– 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).