Using convolution control block for Chinese sentiment analysis

Xiao, Zheng; Wang, Le; Yang, Qiuwei; Du, Jiayi; Sangaiah, Arun Kumar

doi:10.1016/j.jpdc.2017.10.018

Cited by 24 publications

(13 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the different semantic segmentation methods in Chinese and English, many existing methods cannot be directly applied to the task of Chinese text classification. Xiao et al [4] proposed a Chinese sentiment classification model based on the convolution control module CCB. The accuracy on the hotel review data set can be Up to 92.58%.…”

Section: Related Workmentioning

confidence: 99%

“…It is difficult to deal with the emergence of new and unknown words, and it has domain-dependent Question [1]. The machine learning method ignores the order of words in the sentence and cannot distinguish the semantics of the sentence, it leads to the problem of sentiment classification error [4]. For example, the bag of words model [5](BOW), which is more common in machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sentiment Analysis Using Multi-Head Attention Capsules With Multi-Channel CNN and Bidirectional GRU

Chen

Sun

Chen³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Existing text sentiment analysis methods mostly rely on a large number of language knowledge and sentiment resources. This paper proposes the Multi-channel convolution and bidirectional GRU multi-head attention capsule(AT-MC-BiGRU-Capsule), which uses vector neurons to replace scalar neurons to model text emotions, and uses capsules to characterize text emotions. In addition, traditional methods cannot extract the multi-level features of text sequence well. Multi-head attention can encode the dependencies between words, capture sentiment words in text, and using Convolutional Neural Network(CNN) and Bidirectional gated recurrent unit network(Bi-GRU) to extract local features and global semantic features of text respectively, the global average pooling layer is introduced to obtain the multilevel feature representation of the text sequence more comprehensively. This paper selects three English datasets and one Chinese dataset in the general corpus of sentiment classification to conduct experiments, and achieves better results than other baseline models.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis Using Multi-Head Attention Capsules With Multi-Channel CNN and Bidirectional GRU

Chen

Sun

Chen³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Previous studies have attempted to identify sarcasm in a tweet using different feature engineering approaches. Literature on sarcasm detection reveals that these existing methods suffer two main problems [ 12 – 15 ]. One, the context of the words in representation is ignored [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have proposed various feature engineerings methods such as the N-gram, Bag-of-words and word embedding for sarcasm identification in social media [ 14 , 20 , 21 ]. Even though few studies have implemented conventional text classification-based feature engineering methods for sarcasm detection, literature studies [ 12 – 15 ] reveals that most current methods face various issues that need to be resolved to improve the sarcasm identification framework. This includes one, the context of the words is ignored in representation in the sentence since it is only concerned with the occurrence of the word.…”

Section: Introductionmentioning

confidence: 99%

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

2021

View full text Add to dashboard Cite

Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. However, most related techniques have always concentrated only on the content-based features in sarcastic expression, leaving the contextual information in isolation. This leads to a loss of the semantics of words in the sarcastic expression. Another drawback is the sparsity of the training data. Due to the word limit of microblog, the feature vector’s values for each sample constructed by BoW produces null features. To address the above-named problems, a Multi-feature Fusion Framework is proposed using two classification stages. The first stage classification is constructed with the lexical feature only, extracted using the BoW technique, and trained using five standard classifiers, including SVM, DT, KNN, LR, and RF, to predict the sarcastic tendency. In stage two, the constructed lexical sarcastic tendency feature is fused with eight other proposed features for modelling a context to obtain a final prediction. The effectiveness of the developed framework is tested with various experimental analysis to obtain classifiers’ performance. The evaluation shows that our constructed classification models based on the developed novel feature fusion obtained results with a precision of 0.947 using a Random Forest classifier. Finally, the obtained results were compared with the results of three baseline approaches. The comparison outcome shows the significance of the proposed framework.

show abstract

“…In NLP, language modeling is one of the crucial tasks aiming at the assignment of probability to a sequence of words and is used in various domains, including text categorization [2]. An automatic text classification plays a vital role in numerous applications like email spam detection [3] and sentiment classification [4]. To achieve this, an efficient representation of a document is a key step in order to retrieve the associated sentiment.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Classification Using a Single-Layered BiLSTM Model

2020

View full text Add to dashboard Cite

This study presents a computationally efficient deep learning model for binary sentiment classification, which aims to decide the sentiment polarity of people's opinions, attitudes, and emotions expressed in written text. To achieve this, we exploited three widely practiced datasets based on public opinions about movies. We utilized merely one bidirectional long short-term memory (BiLSTM) layer along with a global pooling mechanism and achieved an accuracy of 80.500%, 85.780%, and 90.585% on MR, SST2 and IMDb datasets, respectively. We concluded that the performance metrics of our proposed approach are competitive with the recently published models, having comparatively complex architectures. Also, it is inferred that the proposed single-layered BiLSTM based architecture is computationally efficient and can be recommended for real-time applications in the field of sentiment analysis. INDEX TERMS Bidirectional long short-term memory, deep learning, long-term dependencies, natural language processing, sentiment analysis.

show abstract

Using convolution control block for Chinese sentiment analysis

Cited by 24 publications

References 14 publications

Sentiment Analysis Using Multi-Head Attention Capsules With Multi-Channel CNN and Bidirectional GRU

Sentiment Analysis Using Multi-Head Attention Capsules With Multi-Channel CNN and Bidirectional GRU

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

Sentiment Classification Using a Single-Layered BiLSTM Model

Contact Info

Product

Resources

About