Comparative Study of Deep Learning-Based Sentiment Classification
The purpose of sentiment classification is to determine whether a particular document has a positive or negative nuance. Sentiment classification is extensively used in many business domains to improve products or services by understanding the opinions of customers regarding these products. Deep lea...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8948030/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850190895110946816 |
|---|---|
| author | Seungwan Seo Czangyeob Kim Haedong Kim Kyounghyun Mo Pilsung Kang |
| author_facet | Seungwan Seo Czangyeob Kim Haedong Kim Kyounghyun Mo Pilsung Kang |
| author_sort | Seungwan Seo |
| collection | DOAJ |
| description | The purpose of sentiment classification is to determine whether a particular document has a positive or negative nuance. Sentiment classification is extensively used in many business domains to improve products or services by understanding the opinions of customers regarding these products. Deep learning achieves state-of-the-art results in various challenging domains. With the success of deep learning, many studies have proposed deep-learning-based sentiment classification models and achieved better performances compared with conventional machine learning models. However, one practical issue occurring in deep-learning-based sentiment classification is that the best model structure depends on the characteristics of the dataset on which the deep learning model is trained; moreover, it is manually determined based on the domain knowledge of an expert or selected from a grid search of possible candidates. Herein, we present a comparative study of different deep-learning-based sentiment classification model structures to derive meaningful implications for building sentiment classification models. Specifically, eight deep-learning models, three based on convolutional neural networks and five based on recurrent neural networks, with two types of input structures, i.e., word level and character level, are compared for 13 review datasets, and the classification performances are discussed under different perspectives. |
| format | Article |
| id | doaj-art-86ba8a29f9764b72937a7f6d624bef45 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-86ba8a29f9764b72937a7f6d624bef452025-08-20T02:15:07ZengIEEEIEEE Access2169-35362020-01-0186861687510.1109/ACCESS.2019.29634268948030Comparative Study of Deep Learning-Based Sentiment ClassificationSeungwan Seo0Czangyeob Kim1Haedong Kim2Kyounghyun Mo3Pilsung Kang4https://orcid.org/0000-0001-7663-3937School of Industrial Management Engineering, Korea University, Seoul, South KoreaSchool of Industrial Management Engineering, Korea University, Seoul, South KoreaDepartment of Industrial and Manufacturing Engineering, The Pennsylvania State University, State College, PA, USASK C&C, Seoul, South KoreaSchool of Industrial Management Engineering, Korea University, Seoul, South KoreaThe purpose of sentiment classification is to determine whether a particular document has a positive or negative nuance. Sentiment classification is extensively used in many business domains to improve products or services by understanding the opinions of customers regarding these products. Deep learning achieves state-of-the-art results in various challenging domains. With the success of deep learning, many studies have proposed deep-learning-based sentiment classification models and achieved better performances compared with conventional machine learning models. However, one practical issue occurring in deep-learning-based sentiment classification is that the best model structure depends on the characteristics of the dataset on which the deep learning model is trained; moreover, it is manually determined based on the domain knowledge of an expert or selected from a grid search of possible candidates. Herein, we present a comparative study of different deep-learning-based sentiment classification model structures to derive meaningful implications for building sentiment classification models. Specifically, eight deep-learning models, three based on convolutional neural networks and five based on recurrent neural networks, with two types of input structures, i.e., word level and character level, are compared for 13 review datasets, and the classification performances are discussed under different perspectives.https://ieeexplore.ieee.org/document/8948030/Sentiment classificationdeep learningconvolutional neural networkrecurrent neural networkword embeddingcharacter embedding |
| spellingShingle | Seungwan Seo Czangyeob Kim Haedong Kim Kyounghyun Mo Pilsung Kang Comparative Study of Deep Learning-Based Sentiment Classification IEEE Access Sentiment classification deep learning convolutional neural network recurrent neural network word embedding character embedding |
| title | Comparative Study of Deep Learning-Based Sentiment Classification |
| title_full | Comparative Study of Deep Learning-Based Sentiment Classification |
| title_fullStr | Comparative Study of Deep Learning-Based Sentiment Classification |
| title_full_unstemmed | Comparative Study of Deep Learning-Based Sentiment Classification |
| title_short | Comparative Study of Deep Learning-Based Sentiment Classification |
| title_sort | comparative study of deep learning based sentiment classification |
| topic | Sentiment classification deep learning convolutional neural network recurrent neural network word embedding character embedding |
| url | https://ieeexplore.ieee.org/document/8948030/ |
| work_keys_str_mv | AT seungwanseo comparativestudyofdeeplearningbasedsentimentclassification AT czangyeobkim comparativestudyofdeeplearningbasedsentimentclassification AT haedongkim comparativestudyofdeeplearningbasedsentimentclassification AT kyounghyunmo comparativestudyofdeeplearningbasedsentimentclassification AT pilsungkang comparativestudyofdeeplearningbasedsentimentclassification |