Evaluating the Accuracy and Efficiency of Sentiment Analysis Pipelines with UIMA

Sentiment analysis methods co-ordinate text mining components, such as sentence splitters, tokenisers and classifiers, into pipelined applications to automatically analyse the emotions or sentiment expressed in textual content. However, the performance of sentiment analysis pipelines is known to be substantially affected by the constituent components. In this paper, we leverage the Unstructured Information Management Architecture (UIMA) to seamlessly co ordinate components into sentiment analysis pipelines. We then evaluate a wide range of different combinations of text mining components to identify optimal settings. More specifically, we evaluate different pre-processing components, e.g. tokenisers and stemmers, feature weighting schemes, e.g. TF and TFIDF, feature types, e.g. bigrams, trigrams and bigrams+trigrams, and classification algorithms, e.g. Support Vector Machines, Random Forest and Naive Bayes, against 6 publicly available datasets. The results demonstrate that optimal configurations are consistent across the 6 datasets while our UIMA-based pipeline yields a robust performance when compared to baseline methods.