Improving Sentiment Analysis with Multi-task Learning of Negation
This work addresses the challenge of handling negation in sentiment analysis for natural language processing applications, representing an incremental improvement over existing methods.
The paper tackled the problem of sentiment analysis being affected by negation by proposing a multi-task learning approach that explicitly incorporates negation information, which outperformed implicit learning methods across several standard English-language datasets.
Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most prevalent of these phenomena and in order to correctly predict sentiment, a classifier must be able to identify negation and disentangle the effect that its scope has on the final polarity of a text. This paper proposes a multi-task approach to explicitly incorporate information about negation in sentiment analysis, which we show outperforms learning negation implicitly in a data-driven manner. We describe our approach, a cascading neural architecture with selective sharing of LSTM layers, and show that explicitly training the model with negation as an auxiliary task helps improve the main task of sentiment analysis. The effect is demonstrated across several different standard English-language data sets for both tasks and we analyze several aspects of our system related to its performance, varying types and amounts of input data and different multi-task setups.