Recent advances in deep learning based sentiment analysis

Yuan, Jianhua; Wu, Yang; Lu, Xin; Zhao, Yanyan; Qin, Bing; Liu, Ting

doi:10.1007/s11431-020-1634-3

Cited by 35 publications

(15 citation statements)

References 75 publications

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“…Ordinary users can learn about other people's evaluations of movies through the comment area of the movie platform. In the research center of natural language processing, we need to calculate the joint probability distribution of multiple random variables, and the formula for the joint probability distribution of n random variables can be generalized according to equations ( 1) and (2).…”

Section: P(y|x) � P(x Y) P(x) + P(y)mentioning

confidence: 99%

“…In this example, the evaluation of product and service aspects is related, and each aspect has a di erent emotional bias, so it is not only imprecise to determine the emotional tendency of the whole sentence directly. In general, aspect-level-based sentiment classification is more relevant and valuable and therefore has received attention from a wide range of researchers [2].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Natural Language Processing Methods for Sentiment Analysis in Social Networks

2022

Mathematical Problems in Engineering

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This paper presents an in-depth study of the sentiment of social network communication through a deep learning-based natural language processing approach and designs a corresponding model to be applied in the actual social process. Specifically, the network can dynamically select the most important word in the current state according to the information available and achieve the accurate recognition of the dynamically changing important content in a sentence. Based on this, the semantic understanding of the whole sentence is achieved through a continuous cycle of the process. In addition, considering that the semantic representation of natural language is highly dependent on contextual information, the lack of contextual information will lead to the ambiguity and inaccuracy of semantic representation. In this paper, we study the sentiment analysis algorithms in social networks at two levels, unimodal and multimodal, and construct a text sentiment analysis model and a picture-text multimodal sentiment analysis model in social networks, respectively. By comparing the experiments with the existing models on several datasets, the accuracy of the two models exceeded the benchmark models by 4.45% and 5.2%, respectively, which verified the effectiveness of the two models. The feasibility of applying the optimized convolutional neural network recurrent optimization network to social network sentiment analysis is verified by practically applying the optimized convolutional neural network recurrent optimization network to single task and multitask and comparing other existing deep learning classifiers.

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Section: P(y|x) � P(x Y) P(x) + P(y)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Natural Language Processing Methods for Sentiment Analysis in Social Networks

2022

Mathematical Problems in Engineering

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“…More researchers have begun to do sentiment analysis using deep learning methods, such as bi-directional long short-term memory (BiLSTM), TextCNN, recurrent convolutional neural networks (RCNNs) (Kim, 2014;Zhou et al, 2016;Yang et al, 2019;Yuan et al, 2020), etc. These neural network models can automatically learn the features of the text and have significantly increased classification accuracy over former models; however, by directly mapping the input text to a vector space first and then learning the characteristics of different types of text through the optimization of a matrix used by the neural network model have adequately satisfactory results, but do not emphasize the influence of grammatical rules (Cepukenas et al, 2015) of the input text.…”

Section: Introductionmentioning

confidence: 99%

An Augmented Neural Network for Sentiment Analysis Using Grammar

Zhang

Shang

et al. 2022

Front. Neurorobot.

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Understanding human sentiment from their expressions is very important in human-robot interaction. But deep learning models are hard to represent grammatical changes for natural language processing (NLP), especially for sentimental analysis, which influence the robot's judgment of sentiment. This paper proposed a novel sentimental analysis model named MoLeSy, which is an augmentation of neural networks incorporating morphological, lexical, and syntactic knowledge. This model is constructed from three concurrently processed classical neural networks, in which output vectors are concatenated and reduced with a single dense neural network layer. The models used in the three grammatical channels are convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and fully connected dense neural networks. The corresponding output in the three channels is morphological, lexical, and syntactic results, respectively. Experiments are conducted on four different sentimental analysis corpuses, namely, hotel, NLPCC2014, Douban movie reviews dataset, and Weibo. MoLeSy can achieve the best performance over previous state-of-art models. It indicated that morphological, lexical, and syntactic grammar can augment the neural networks for sentimental analysis.

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“…With the advent of the information age, enormous data is generated by users on the Internet in real time. It is important to utilize the data for sentiment analysis to achieve public opinion monitoring, stock market prediction, and consumption preference analysis [1]. Due to the diversity of social information, multimodal sentiment analysis has attracted great attention from researchers.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Blog Sentiment Classification Based on MD-HFCE

Yang

Tian

2021

Security and Communication Networks

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In recent years, the rapid growth of multimodal information has become an important factor affecting the results of sentiment analysis. However, a few state-of-the-art works take into account the multimodal features and sentiment fuzziness. To this end, a fuzzy method is proposed for assessing sentiment intensity in this paper. Firstly, based on the visual-text conversion network (CNN-LSTM), as well as sentiment optimization through SentiBank and SentiBridge, the visual features are normalized to the text features. At the same time, the emotional features of the extracted audio will be predicted by the random forest algorithm. Subsequently, the sentiment characteristics are processed by dual hesitant fuzzification to form positive and negative sentiment intensity factors. Finally, a classification method, that is, MD-HFCE (multilayer dual hesitant fuzzy comprehensive evaluation), fuzzy comprehensive evaluation method improved by Mamdani fuzzy reasoning, is proposed to realize the multifeature fuzzy sentiment classification based on the comprehensive sentiment dictionary. The classification results are applicable to the topics of sentiment monitoring. The experimental results show that the proposed algorithm can effectively realize feature integration and improve the average sentiment classification accuracy of multimodal blogs to 82.2%.

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Recent advances in deep learning based sentiment analysis

Cited by 35 publications

References 75 publications

Deep Learning-Based Natural Language Processing Methods for Sentiment Analysis in Social Networks

Deep Learning-Based Natural Language Processing Methods for Sentiment Analysis in Social Networks

An Augmented Neural Network for Sentiment Analysis Using Grammar

Multimodal Blog Sentiment Classification Based on MD-HFCE

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