Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism

10Citations
Citations of this article
25Readers
Mendeley users who have this article in their library.

Abstract

The aim of aspect-level sentiment analysis is to identify the sentiment polarity of a given target term in sentences. Existing neural network models provide a useful account of how to judge the polarity. However, context relative position information for the target terms is adversely ignored under the limitation of training datasets. Considering position features between words into the models can improve the accuracy of sentiment classification. Hence, this study proposes an improved classification model by combining multilevel interactive bidirectional Gated Recurrent Unit (GRU), attention mechanisms, and position features (MI-biGRU). Firstly, the position features of words in a sentence are initialized to enrich word embedding. Secondly, the approach extracts the features of target terms and context by using a well-constructed multilevel interactive bidirectional neural network. Thirdly, an attention mechanism is introduced so that the model can pay greater attention to those words that are important for sentiment analysis. Finally, four classic sentiment classification datasets are used to deal with aspect-level tasks. Experimental results indicate that there is a correlation between the multilevel interactive attention network and the position features. MI-biGRU can obviously improve the performance of classification.

References Powered by Scopus

GloVe: Global vectors for word representation

26881Citations
N/AReaders
Get full text

Bidirectional recurrent neural networks

7361Citations
N/AReaders
Get full text

Attention-based LSTM for aspect-level sentiment classification

2146Citations
N/AReaders
Get full text

Cited by Powered by Scopus

A novel network with multiple attention mechanisms for aspect-level sentiment analysis

56Citations
N/AReaders
Get full text

MAPA BiLSTM-BERT: multi-aspects position aware attention for aspect level sentiment analysis

15Citations
N/AReaders
Get full text

CoreNLP dependency parsing and pattern identification for enhanced opinion mining in aspect-based sentiment analysis

9Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Wang, X., Chen, X., Tang, M., Yang, T., & Wang, Z. (2020). Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism. Discrete Dynamics in Nature and Society, 2020. https://doi.org/10.1155/2020/5824873

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 7

70%

Lecturer / Post doc 3

30%

Readers' Discipline

Tooltip

Computer Science 13

93%

Linguistics 1

7%

Save time finding and organizing research with Mendeley

Sign up for free