Research Area:  Machine Learning

Abstract:

Along with the emergence of the Internet, the rapid development of handheld devices has democratized content creation due to the extensive use of social media and has resulted in an explosion of short informal texts. Although a sentiment analysis of these texts is valuable for many reasons, this task is often perceived as a challenge given that these texts are often short, informal, noisy, and rich in language ambiguities, such as polysemy. Moreover, most of the existing sentiment analysis methods are based on clean data. In this paper, we present , a transformer-based method for sentiment analysis that encodes representation from a transformer and applies deep intelligent contextual embedding to enhance the quality of tweets by removing noise while taking word sentiments, polysemy, syntax, and semantic knowledge into account. We also use the bidirectional long- and short-term memory network to determine the sentiment of a tweet. To validate the performance of the proposed framework, we perform extensive experiments on three benchmark datasets, and results show that considerably outperforms the state of the art in sentiment classification.

Keywords:  

Author(s) Name:  Usman Naseem, Imran Razzak, KatarzynaMusial,Muhammad Imran

Publisher name:  ELSEVIER

DOI:  10.1016/j.future.2020.06.050

Volume Information:  Volume 113, December 2020, Pages 58-69

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0167739X2030306X