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Domain specific sentiment analysis using cross-domain data

Sentiment analysis is a tool used to guide critical business and engineering decisions. Such applications demand sentiment analysis specialized in specific domain areas. In this work we investigate the effectiveness of standard machine learning techniques and feature engineering that can effectively be applied in the industry. Further we investigate the use of labeled data from multiple domain areas to improve the results in a specific domain area in a product review setting. Finally we propose a method based on dimensionality reduction and clustering to reduce the size of the problem without loss in performance.