Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network

Mao, Yanying; Zhang, Yu; Jiao, Liudan; Zhang, Heshan

doi:10.3390/electronics11121906

Cited by 13 publications

(3 citation statements)

References 41 publications

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“…On the whole, the translation accuracy of the proposed model is relatively stable and has little fluctuation. Literature [34] This research Literature [33] Literature [32] Fig. 10 The translation accuracy of each model…”

Section: Model Test Resultsmentioning

confidence: 99%

Intelligent Text Sentiment Analysis of Emotional Beauty in the English Translation: Based on TB-LSTM Algorithm

2024

JAIT

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In the existing deep learning sentiment analysis methods, text is directly processed into a sequence of words, which often ignores the important components of the text and the topic of the text. To solve these problems, the topic of text is extracted through Latent Dirichlet Allocation model, and then a method for integrating topic information into neural networks is proposed. Furthermore, an emotion classification model that integrates the topic information is constructed. An attention mechanism is used at the lexical level and the sentence level, paying attention to the more and less important content separately when building the document presentation. The results show that the proposed model has excellent performance in all four kinds of data, among which the accuracy rate, recall rate and F1-score of CCF-BDC are the best, reaching 0.63, 0.72 and 0.7 respectively. The translation accuracy also reaches 0.85. These results show that the model proposed in this study has better performance in sentiment analysis. These research results will help improve the user's reading experience and make machine translation better meet the needs of human language expression.

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Section: Model Test Resultsmentioning

confidence: 99%

Intelligent Text Sentiment Analysis of Emotional Beauty in the English Translation: Based on TB-LSTM Algorithm

2024

JAIT

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“…This approach highlighted the issue of modelling the long text to consider the intrinsic relationship between the sentences in the semantic meaning of the document. It was solved using a novel neural network model called AttBiLSTM-2DCNN [8]. Authors Wang and Manning in their article proposed a simple, yet effective approach for sentiment and topic classification based on baselines and bigrams.…”

Section: Literaturementioning

confidence: 99%

Sentimental Analysis of Movie Reviews Using Machine Learning

Sharma¹,

Pangaonkar²,

Gunjan

et al. 2023

ITM Web Conf.

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Sentiment analysis is a rapidly growing field in natural language processing that aims to extract subjective information from text data. One of the most common applications of sentiment analysis is in the movie industry, where it is used to gauge public opinion on films. In this research paper, a sentimental analysis of movie reviews has been presented using a dataset of over 25,000 reviews collected from various sources. A machine learning model with different classifiers was built using Naïve Bayes, Logistic Regression and Support Vector Machines for classifying movie reviews as positive, negative or neutral. A comparison of three popular machine learning algorithms was made. After pre-processing the dataset by removing stop words, a stemming technique was applied to reduce the dimensionality of the dataset. The recognition algorithms were evaluated in terms of performance matrices such as accuracy, precision, recall and F1-score. Compared to others, it was observed that the SVM algorithm performed the best among all three algorithms, achieving an accuracy of 73%. The results of this analysis demonstrated the eﬀectiveness of the model in accurately classifying movie reviews and provided valuable insights into the current state of public opinion on films. The comparison of the three algorithms provided insight into the best algorithm to be used for a specific dataset and scenario.

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“…The state-of-the-art solutions for sentiment analysis at different granularities have been built upon DNN models. For instance, for document-level sentiment analysis, the DNN models include CSNN 9 , AttBiLSTM-2DCNN 10 , CNN-BiLSTM 11 , SR-LSTM 12 and BAE 13 ; for aspect-level sentiment analysis, the most recent DNN models include LCF-BERT 14 , PTMs 15 and RGAT 16 , all of which are variants of the pre-trained BERT model. This paper focuses on sentiment analysis at sentence level.…”

Section: Related Workmentioning

confidence: 99%

Sentence-level sentiment analysis based on supervised gradual machine learning

Su,

Chen,

Wang

et al. 2023

Sci Rep

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Sentence-level sentiment analysis (SLSA) aims to identify the overall sentiment polarity conveyed in a given sentence. The state-of-the-art performance of SLSA has been achieved by deep learning models. However, depending on the i.i.d (independent and identically distributed) assumption, the performance of these deep learning models may fall short in real scenarios, where the distributions of training and target data are almost certainly different to some extent. In this paper, we propose a supervised solution based on the non-i.i.d paradigm of gradual machine learning (GML) for SLSA. It begins with some labeled observations, and gradually labels target instances in the order of increasing hardness by iterative knowledge conveyance. It leverages labeled samples for supervised deep feature extraction, and constructs a factor graph based on the extracted features to enable gradual knowledge conveyance. Specifically, it employs a polarity classifier to detect polarity similarity between close neighbors in an embedding space, and a separate binary semantic network to extract implicit polarity relations between arbitrary instances. Our extensive experiments on benchmark datasets show that the proposed approach achieves the state-of-the-art performance on all benchmark datasets. Our work clearly demonstrates that by leveraging DNN for feature extraction, GML can easily outperform the pure DNN solutions.

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Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network

Cited by 13 publications

References 41 publications

Intelligent Text Sentiment Analysis of Emotional Beauty in the English Translation: Based on TB-LSTM Algorithm

Intelligent Text Sentiment Analysis of Emotional Beauty in the English Translation: Based on TB-LSTM Algorithm

Sentimental Analysis of Movie Reviews Using Machine Learning

Sentence-level sentiment analysis based on supervised gradual machine learning

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