  Existing aspect based sentiment analysis (ABSA) approaches leverage various
neural network models to extract the aspect sentiments via learning
aspect-specific feature representations. However, these approaches heavily rely
on manual tagging of user reviews according to the predefined aspects as the
input, a laborious and time-consuming process. Moreover, the underlying methods
do not explain how and why the opposing aspect level polarities in a user
review lead to the overall polarity. In this paper, we tackle these two
problems by designing and implementing a new Multiple-Attention Network (MAN)
approach for more powerful ABSA without the need for aspect tags using two new
tag-free data sets crawled directly from TripAdvisor
({https://www.tripadvisor.com}). With the Self- and Position-Aware attention
mechanism, MAN is capable of extracting both aspect level and overall
sentiments from the text reviews using the aspect level and overall customer
ratings, and it can also detect the vital aspect(s) leading to the overall
sentiment polarity among different aspects via a new aspect ranking scheme. We
carry out extensive experiments to demonstrate the strong performance of MAN
compared to other state-of-the-art ABSA approaches and the explainability of
our approach by visualizing and interpreting attention weights in case studies.
