Our paper “Evaluating Industrial and Research Sentiment Analysis Engines on Multiple Sources” has been selected for the AIxIA2017.
Sentiment Analysis has a fundamental role in analyzing users opinions in all kinds of textual sources. Computing accurately sentiment expressed in huge amount of textual data is a key task largely required by the market, and nowadays industrial engines make available ready-to-use APIs for sentiment analysis-related tasks.
In this paper, we compare the results of research and industrial engines considering the document-level polarity detection task performed on different textual sources: tweets, apps reviews and general products reviews.
The experimental evaluation results help the reader to quantify the performance gap between industrial and research sentiment engines when both are tested on heterogeneous textual sources and on different languages (English/Italian).
Learn more at http://aiia2017.di.uniba.it/