Related Tasks can Share! A Multi-task Framework for Affective language
This work addresses sentiment analysis for natural language processing applications, but it is incremental as it combines existing methods without introducing a new paradigm.
The paper tackles the problem of sentiment analysis by jointly learning sentiment classification and intensity prediction in a multi-task framework, resulting in improved performance over single-task approaches.
Expressing the polarity of sentiment as 'positive' and 'negative' usually have limited scope compared with the intensity/degree of polarity. These two tasks (i.e. sentiment classification and sentiment intensity prediction) are closely related and may offer assistance to each other during the learning process. In this paper, we propose to leverage the relatedness of multiple tasks in a multi-task learning framework. Our multi-task model is based on convolutional-Gated Recurrent Unit (GRU) framework, which is further assisted by a diverse hand-crafted feature set. Evaluation and analysis suggest that joint-learning of the related tasks in a multi-task framework can outperform each of the individual tasks in the single-task frameworks.