A Sentiment Classification Method of Web Social Media Based on Multidimensional and Multilevel Modeling

Wang, Bingkun; Shan, Donghong; Fan, Aiwan; Liu, Lei; Gao, Jingli

doi:10.1109/tii.2021.3085663

Cited by 11 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MDMLSM: This model firstly uses the pre-trained BERT model to form word vectors, then applies the attention-based BiLSTM to extract text features, and finally the output feature representations are sequentially input into the multilayer perceptron and sentiment classifier [ 59 ].…”

Section: Methodsmentioning

confidence: 99%

Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis

Wang¹,

Meng²

2022

Sensors

View full text Add to dashboard Cite

In Chinese sentiment analysis tasks, many existing methods tend to use recurrent neural networks (e.g., long short-term memory networks and gated recurrent units) and standard one-dimensional convolutional neural networks (1D-CNN) to extract features. This is because a recurrent neural network can deal with the order dependence of the data to a certain extent and the one-dimensional convolution can extract local features. Although these methods have good performance in sentiment analysis tasks, recurrent neural networks (RNNs) cannot be parallelized, resulting in time-inefficiency, and the standard 1D-CNN can only extract a single sample feature, with the result that the feature information cannot be fully utilized. To this end, in this paper, we propose a multichannel two-dimensional convolutional neural network based on interactive features and group strategy (MCNN-IFGS) for Chinese sentiment analysis. Firstly, we no longer use word encoding technology but use character-based integer encoding to retain more fine-grained information. Besides, in character-level vectors, the interactive features of different elements are introduced to improve the dimensionality of feature vectors and supplement semantic information so that the input matches the model network. In order to ensure that more sentiment features are learned, group strategies are used to form several feature mapping groups, so the learning object is converted from the traditional single sample to the learning of the feature mapping group, so as to achieve the purpose of learning more features. Finally, multichannel two-dimensional convolutional neural networks with different sizes of convolution kernels are used to extract sentiment features of different scales. The experimental results on the Chinese dataset show that our proposed method outperforms other baseline and state-of-the-art methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis

Wang¹,

Meng²

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The results show that, in most cases, users are in favour of SUS. Wang et al [10] considering the emoticon symbols and punctuation symbols in web social media text. Similar to language symbols, emoticons' symbols and punctuation symbols in web social media text also contain certain sentiment information.…”

Section: IImentioning

confidence: 99%

Opinion Mining For E-Commerce in Social Media Accounts

S¹,

V²,

Desvar³

et al. 2023

IJRASET

View full text Add to dashboard Cite

The usage of the Internet has been growing rapidly, and in the field of natural language processing (NLP), sentiment analysis has become one of the most popular techniques. Using sentiment analysis, the emotional tone behind a body of text can be mined effectively for different occasions. Instagram has become a popular platform for people to buy and sell online. Nonetheless, some studies have found that frauds occurred as a result of buying and selling on the platform, such as when product quantity and specifications differed from what was claimed, a defective product was received, and other frauds involved Instagram sellers. Hence, trust is vital when customers are engaged in S-Commerce activities on Instagram, and there is a need for a trust model to evaluate the trustworthiness of sellers. Nowadays, people provide their feedback after a long time of usage only on social media platforms like Instagram. Therefore, these comments play a major role in helping people decide if the product will be beneficial for them over a long period of time. Mining such content to evaluate people’s sentiments can play a critical role in making decisions to keep the situation under control. {objective} Six different classifiers have been used to classify the data. The experiment achieved the highest accuracy of 76.7% with logistic regression. This study can be useful to determine the trustworthiness of the company.

show abstract

“…To address these limitations, deep learning, a cluster of multi-layer neural network algorithms have emerged as a promising sub-field of machine learning for Twitter sentiment analysis [32,33,34]. Several deep learning-based models, including Deep (Vanilla) Neural Networks (DNN) Ali et al [32], Yasir et al [34], Convolutional Neural Networks (CNN) [35,36,37,38], Recurrent Neural Networks (RNN) [39,40], and their variants such as Long Short-Term Memory (LSTM) [41,42,43,44], Gated Recurrent Units (GRU) and hybrid techniques have shown effectiveness in capturing the nuances of natural language and handling the noise and ambiguity present in Twitter data [35,36,37,38,39,40,41,42,43,44]. These models offer flexible solutions that enhance sentiment analysis performance by providing a better interpretation of the context and semantic meaning of text data.…”

Section: Introductionmentioning

confidence: 99%

A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

Chaudhary,

Girdhar,

Sharma

et al. 2024

IEEE Trans. Comput. Soc. Syst.

View full text Add to dashboard Cite

Microblogging site Twitter is one of the most influential online social media websites, that offers a platform for the masses to communicate, express their opinions, and share information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers, who seek to understand and analyze the sentiments contained within this massive user-generated text. The task of sentiment analysis entails extracting and identifying user opinions from the text, and various lexicon and machine learning-based methods have been developed over the years to accomplish this. However, deep learning-based approaches have recently become dominant due to their superior performance. The current study briefs on standard preprocessing techniques and various word embeddings for data preparation. It then delves into a taxonomy to provide a comprehensive summary of deep learning-based approaches. Additionally, the work compiles popular benchmark datasets and highlights evaluation metrics employed for performance measures as well as the resources available in the public domain to aid sentiment analysis tasks. Furthermore, the survey discusses domain-specific practical applications of sentiment analysis tasks. Finally, the study concludes with various research challenges and outlines future outlooks for further investigation.

show abstract

A Sentiment Classification Method of Web Social Media Based on Multidimensional and Multilevel Modeling

Cited by 11 publications

References 22 publications

Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis

Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis

Opinion Mining For E-Commerce in Social Media Accounts

A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

Contact Info

Product

Resources

About