A Short Text Classification Method Based on
            <i>N</i>
            ‐Gram and CNN

Wang, Haitao; He, Jie; Zhang, Xiaohong; Liu, Shufen

doi:10.1049/cje.2020.01.001

Cited by 65 publications

(32 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These models can predict author sentiment from text. Early language models mainly used the bag-of-words model [3] or the N-Gram method [4] to obtain text representations based on word frequency or co-occurrence window statistics, and then calculate text emotions; in addition, there are a lot of researches by extracting emotional keywords and designing a keyword library for text emotion detection [5,6] .…”

Section: Sentiment Analysis Based On Textmentioning

confidence: 99%

Fine-grained Multimodal Sentiment Analysis Based on Gating and Attention Mechanism

Sun¹,

Gao²

2021

Electron Sci Technol Appl

View full text Add to dashboard Cite

<p>In recent years, more and more people express their feelings through both images and texts, boosting the growth of multimodal data. Multimodal data contains richer semantics and is more conducive to judging the real emotions of people. To fully learn the features of every single modality and integrate modal information, this paper proposes a fine-grained multimodal sentiment analysis method FCLAG based on gating and attention mechanism. First, the method is carried out from the character level and the word level in the text aspect. CNN is used to extract more fine-grained emotional information from characters, and the attention mechanism is used to improve the expressiveness of the keywords. In terms of images, a gating mechanism is added to control the flow of image information between networks. The images and text vectors represent the original data collectively. Then the bidirectional LSTM is used to complete further learning, which enhances the information interaction capability between the modalities. Finally, put the multimodal feature expression into the classifier. This method is verified on a self-built image and text dataset. The experimental results show that compared with other sentiment classification models, this method has greater improvement in accuracy and F1 score and it can effectively improve the performance of multimodal sentiment analysis.</p>

show abstract

Section: Sentiment Analysis Based On Textmentioning

confidence: 99%

Fine-grained Multimodal Sentiment Analysis Based on Gating and Attention Mechanism

Sun¹,

Gao²

2021

Electron Sci Technol Appl

View full text Add to dashboard Cite

show abstract

“…It has proven to be beneficial in detecting fake news for reducing misinformation risks [8]. Various classifiers have been applied on social media articles to classify news as fake using NLP techniques such as N-gram and CNN [9] or bag-of-words [10]. It has been especially worthwhile to apply NLP on human rights related social media articles [11].…”

Section: A Nlp Approaches To Recent Problemsmentioning

confidence: 99%

“…Ref. [7], [9], [11] illustrate how to use NLP and classification techniques on languages other than English.…”

Section: A Nlp Approaches To Recent Problemsmentioning

confidence: 99%

Understanding the Applications of Natural Language Processing on COVID-19 Data

Verma

Paul

Kariyannavar

et al. 2020

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

View full text Add to dashboard Cite

In the digital age, the Internet has enabled the circulation of ideas and information and, in turn, has increased awareness among people. However, this does not come with its drawbacks. With the proliferation of online platforms, hoaxers can easily lure people towards their propagandist views or false news. The need to root out such false information and hate speech during this COVID-19 pandemic has never been more essential. The following study presents a survey of various papers that attempt to tackle similar problem statements with fake news, sentiment classification, and topic extraction. The paper focuses on how existing quality research can help improve the current state of research on COVID-19 related datasets by guiding researchers towards valuable procedures to help governmental authorities combat the rise in the spread of false news and malicious and hate comments.

show abstract

“…From the experimental data in the above table, it can be seen that when four convolution kernels [1,3,5,7] were utilized, the accuracy, precision, recall rate, and F1 value of the model were slightly higher than other convolution kernel combination methods. An analysis of the experimental process of these 7 types of convolution kernels is shown in Figure 9.…”

Section: Experiments Withmentioning

confidence: 99%

“…ese deep learning methods include convolutional neural networks, recurrent neural networks, and attention mechanisms. CNN [3] and RNN [4] are common deep learning methods in the field of natural language processing. CNN have the capacity to realize the learning and representation of data sample features well through "end-to-end" learning.…”

Section: Introductionmentioning

confidence: 99%

Chinese Microblog Sentiment Detection Based on CNN-BiGRU and Multihead Attention Mechanism

Qiuhong¹,

FanChongdi

YaoJie³

et al. 2020

Scientific Programming

View full text Add to dashboard Cite

With the rapid development of the Internet, Weibo has gradually become one of the commonly used social tools in society at present. We can express our opinions on Weibo anytime and anywhere. Weibo is widely used and people can express themselves freely on it; thus, the amount of comments on Weibo has become extremely large. In order to count up the attitudes of users towards a certain event, Weibo managers often need to evaluate the position of a certain microblog in an appropriate way. In traditional position detection tasks, researchers mainly mine text semantic features through constructing feature engineering and sentiment dictionary, but it takes a large amount of manpower in feature selection and design. However, it is an effective method to analyze the sentiment state of microblog comments. Deep learning is developing in an increasingly mature direction, and the utilization of deep learning methods for sentiment detection has become increasingly popular. The application of convolutional neural networks (CNN), bidirectional GRU (BiGRU), and multihead attention mechanism- (multihead attention-) combined method CNN-BiGRU-MAttention (CBMA) to conduct Chinese microblog sentiment detection was proposed in this paper. Firstly, CNN were applied to extract local features of text vectors. Afterward, BiGRU networks were applied to extract the global features of the text to solve the problem that the single CNN cannot obtain global semantic information and the disappearance of the traditional recurrent neural network (RNN) gradient. At last, it was concluded that the CBMA algorithm is more accurate for Chinese microblog sentiment detection through a variety of algorithm experiments.

show abstract

A Short Text Classification Method Based on N ‐Gram and CNN

Cited by 65 publications

References 4 publications

Fine-grained Multimodal Sentiment Analysis Based on Gating and Attention Mechanism

Fine-grained Multimodal Sentiment Analysis Based on Gating and Attention Mechanism

Understanding the Applications of Natural Language Processing on COVID-19 Data

Chinese Microblog Sentiment Detection Based on CNN-BiGRU and Multihead Attention Mechanism

Contact Info

Product

Resources

About