ALBERTC-CNN Based Aspect Level Sentiment Analysis

Xingxin Ye,Yang Xu,Mengshi Luo

doi:10.1109/access.2021.3094026

Xingxin Ye, Yang Xu + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3094026

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Abstract

In order to solve the problem that most aspect level sentiment analysis networks cannot extract the global and local information of the context at the same time. This study proposes an aspect level sentiment analysis model named Combining with A Lite Bidirection Encoder Represention from TransConvs and ConvNets(ALBERTC-CNN). First, the global sentence information and local emotion information in a text are extracted by the improved ALBERTC network, and the input aspect level text is represented by a word vector. Then, the feature vector is mapped to the emotion classification number by a linear function and a softmax function. Finally, the aspect level sentiment analysis results are obtained. The proposed model is tested on two datasets of the SemEval-2014 open task, the laptop and restaurant datasets, and compared with the traditional networks. The results show that compared with the traditional network, the classification accuracy of the proposed model is improved by approximately 4% and 5% on the two sets, whereas the F1 value is improved by approximately 4% and 8%. Additionally, compared with the original ALBERT network, the accuracy is improved by approximately 2%, and the F1 value is improved by approximately 1%.

Highlights

Sentiment analysis is one of the important research tasks in the field of natural language processing, and it has been a hot research direction
Using the aspect level sentiment analysis can provide improvements in many fields, so it is of great significance to study the aspect level sentiment analysis
The global semantic information and local emotional information cannot be extracted simultaneously by most networks, so this study proposes an improved aspect level emotion analysis model based on the ALBERT

Summary

INTRODUCTION

Sentiment analysis is one of the important research tasks in the field of natural language processing, and it has been a hot research direction. The second category includes machine learning-based methods, such as those proposed by Álvarez-López[3], Mubarok[4], Rafeek [5], which use the support vector machine method[6], naive Bayes method[7], and logical regression method[8], respectively, to analyze each sentence part These methods can improve the accuracy of emotion analysis to a certain extent but need to be combined with complex feature engineering for feature annotation and extraction, which requires much manpower, many material and financial resources, and the methods are not highly mobile, requiring heavy training for different tasks. Chen[11] and Xue[12] applied these two network types to aspect level emotion analysis and achieved good results These methods can effectively solve the problems of gradient explosion and gradient disappearance, but the accuracy needs further improvement.

PREPARATION PROPOSED NETWORK

EXPERIMENTAL PARAMETERS AND EVALUATION

ABLATION RESULTS

CONCLUSION