Short text opinion detection using ensemble of classifiers and semantic indexing

• An ensemble system to perform opinion detection in short text messages is proposed.
• The model combines the state-of-the-art classification methods and NLP techniques.
• The proposed ensemble can improve performance of the most text categorization tasks.
• Experimental results on nine real English public datasets are reported.
• The proposed method is statistically superior to the compared approaches.

The popularity of social networks has attracted attention of companies. The growing amount of connected users and messages posted per day make these environments fruitful to detect needs, tendencies, opinions, and other interesting information that can feed marketing and sales departments. However, the most social networks impose size limit to messages, which lead users to compact them by using abbreviations, slangs, and symbols. As a consequence, these problems impact the sample representation and degrade the classification performance. In this way, we have proposed an ensemble system to find the best way to combine the state-of-the-art text processing approaches, as text normalization and semantic indexing techniques, with traditional classification methods to automatically detect opinion in short text messages. Our experiments were diligently designed to ensure statistically sound results, which indicate that the proposed system has achieved a performance higher than the individual established classifiers.