Sentiment-based masked language modeling for improving sentence-level valence–arousal prediction

Wu, Jheng-Long; Chung, Wei-Yi

doi:10.1007/s10489-022-03384-9

Cited by 9 publications

(8 citation statements)

References 46 publications

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“…Meanwhile, Bollen, Mao & Zeng (2011) discovered that the content of Twitter posts is linked to the Dow Jones Industrial Average Index (DJIA), while Liu et al (2017) employed sentiment analysis of posts from the China Stock Forum and predicted the volatility of the Chinese stock market. Their results confirmed that sentiment indicators could effectively enhance the accuracy of the prediction [25].…”

Section: Literature Surveymentioning

confidence: 63%

An Efficient LSTM-Based Deep Learning Model for Stock Prediction Analytics and Real-time Visualization

Saravanan

Paudel

Acharya

et al. 2023

Preprint

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Artificial intelligence (AI) is widely implemented in finance for stock price prediction using deep learning techniques. However, stock data’s high volatility and signal-to-noise ratio can make it difficult for neural networks (NN) to make accurate predictions. Nevertheless, even 70% accuracy can provide a good return value, leading to the increasing adoption of AI. In this study, we utilized long short-term memory (LSTM), a time series forecasting method, to predict the future stock market prices of Twitter (TWTR) and Facebook (FB) on NYSE and evaluated the performance of the model. Additionally, sentiment analysis, which involves understanding information from sentiment-rich sources such as news and social media, plays a crucial role in predicting changes in stock market prices. With the abundance of data in modern times, manual data examination is no longer practical, necessitating AI and data mining techniques. The impact of emotion classification on stock market prices was also investigated in this study. The deep learning method involved using an LSTM neural network to learn the features instead of relying on human feature engineering. The NLP model used in this study combines LSTM, an intriguing branch of machine learning that uses past data to predict future values, with real-time visualization for sentiment analysis.

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Section: Literature Surveymentioning

confidence: 63%

An Efficient LSTM-Based Deep Learning Model for Stock Prediction Analytics and Real-time Visualization

Saravanan

Paudel

Acharya

et al. 2023

Preprint

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“…Swathi et al [33] presented TLBO and LSTM models for stock price prediction using the Twitter dataset. Wu et al [1] proposed the DVA-BERT model for sentiment analysis which outperforms the BERT model. Wu et al [34] proposed a framework for hotel selection based on the OVO-SVM algorithm and Word2Vec.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sentiment analysis is a subfield of Natural Language Processing (NLP) that focuses on the automated analysis of human sentiments [1]. It is possible to examine and categorize a person's emotions using sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews

Punetha

Jain

2023

Appl Intell

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Sentiment Analysis is a method to identify, extract, and quantify people's feelings, opinions, or attitudes. The wealth of online data motivates organizations to keep tabs on customers' opinions and feelings by turning to sentiment analysis tasks. Along with the sentiment analysis, the emotion analysis of written reviews is also essential to improve customer satisfaction with restaurant service. Due to the availability of massive online data, various computerized methods are proposed in the literature to decipher text sentiments. The majority of current methods rely on machine learning, which necessitates the pre-training of large datasets and incurs substantial space and time complexity. To address this issue, we propose a novel unsupervised sentiment classification model. This study presents an unsupervised mathematical optimization framework to perform sentiment and emotion analysis of reviews. The proposed model performs two tasks. First, it identifies a review's positive and negative sentiment polarities, and second, it determines customer satisfaction as either satisfactory or unsatisfactory based on a review. The framework consists of two stages. In the first stage, each review's context, rating, and emotion scores are combined to generate performance scores. In the second stage, we apply a non-cooperative game on performance scores and achieve Nash Equilibrium. The output from this step is the deduced sentiment of the review and the customer's satisfaction feedback. The experiments were performed on two restaurant review datasets and achieved state-of-the-art results. We validated and established the significance of the results through statistical analysis. The proposed model is domain and language-independent. The proposed model ensures rational and consistent results.

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“…In general, sentiment intensity prediction methods can be summarized into four types: lexicon-based [4,[10][11][12][13][14][15][16], regression-based [17][18][19][20][21][22], neural-network-based [23][24][25][26][27][28][29][30][31][32][33], and transformer-based [34][35][36][37][38][39][40][41][42][43]. The lexicon-based approaches provide baseline results for reference, while transformer-based models have usually achieved promising results in the valence-arousal dimensions [9].…”

Section: Introductionmentioning

confidence: 99%

Toward Transformer Fusions for Chinese Sentiment Intensity Prediction in Valence-Arousal Dimensions

Deng,

Wang,

Chen

et al. 2023

IEEE Access

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BERT (Bidirectional Encoder Representations from Transformers) uses an encoder architecture with an attention mechanism to construct a transformer-based neural network. In this study, we develop a Chinese word-level BERT to learn contextual language representations and propose a transformer fusion framework for Chinese sentiment intensity prediction in the valence-arousal dimensions. Experimental results on the Chinese EmoBank indicate that our transformer-based fusion model outperforms other neuralnetwork-based, regression-based and lexicon-based methods, reflecting the effectiveness of integrating semantic representations in different degrees of linguistic granularity. Our proposed transformer fusion framework is also simple and easy to fine-tune over different downstream tasks. INDEX TERMSTransformer fusion, Chinese word-level BERT, pre-trained language models, dimensional sentiment analysis, affective computing.

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Sentiment-based masked language modeling for improving sentence-level valence–arousal prediction

Cited by 9 publications

References 46 publications

An Efficient LSTM-Based Deep Learning Model for Stock Prediction Analytics and Real-time Visualization

An Efficient LSTM-Based Deep Learning Model for Stock Prediction Analytics and Real-time Visualization

Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews

Toward Transformer Fusions for Chinese Sentiment Intensity Prediction in Valence-Arousal Dimensions

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