10SENT: A stable sentiment analysis method based on the combination of off-the-shelf approaches

Sentiment analysis has become a very important tool for analysis of social media data. There are several methods developed, covering distinct aspects of the problem and disparate strategies. However, no single technique fits well in all cases or for all data sources. Supervised approaches may be able to adapt to specific situations, but require manually labeled training, which is very cumbersome and expensive to acquire, mainly for a new application. In this context, we propose to combine several popular and effective state-of-the-practice sentiment analysis methods by means of an unsupervised bootstrapped strategy. One of our main goals is to reduce the large variability (low stability) of the unsupervised methods across different domains. The experimental results demonstrate that our combined method (aka, 10SENT) improves the effectiveness of the classification task, considering thirteen different data sets. Also, it tackles the key problem of cross-domain low stability and produces the best (or close to best) results in almost all considered contexts, without any additional costs (e.g., manual labeling). Finally, we also investigate a transfer learning approach for sentiment analysis to gather additional (unsupervised) information for the proposed approach, and we show the potential of this technique to improve our results.