How good are commercial sentiment analysis tools? We recently tackeled this question in our research team, and evaluated the quality of 9 state-of-the-art commercial sentiment detection tools. We applied them to 30,000 short texts from various sources (tweets, news headlines, reviews etc.). The best tools have an accuracy of 75% for some document types (tweets), but the average accuracy over all documents is at best 60%. This means that even with the best tool, 4 out of 10 documents will be classified wrong.
Since we were convinced that there is still some “potential” for improvement, we combined all tools with a meta-classifier. It turned out that using a random forest classifier can improve accuracy by up to 9 percent points, in comparison to the best single tool.
Our results were published at ESSEM 2013. For more details, please see our paper.