Aspect-Based Sentiment Analysis of Twitter Influencers to Predict the Trend of Cryptocurrencies Based on Hybrid Deep Transfer Learning Models

Abstract

With the expansion of social networks, sentiment analysis has become one of the hot topics in machine learning. However, in traditional sentiment analysis, the text is considered of a general nature and ignores the different aspects that may exist in the text. This paper presents a hybrid model of transfer deep learning methods for the aspect-oriented sentiment analysis of influencers' tweets to predict the trend of cryptocurrencies. In the first model, different aspects of tweets are extracted using the Concept Latent Dirichlet Allocation (Concept-LDA). Then, by using the pre-trained RoBERTa network and combining it with the Bidirectional Gated Recurrent Unit (BiGRU) deep learning network and attention layer, sentiments of different aspects of tweets are determined. In the following, the price trend of seven cryptocurrencies, Bitcoin, Ethereum, Binance, Ripple, Dogecoin, Cardano, and Solana, is determined using the historical price and the polarity of tweets with BiGRU combined deep neural network and the attention layer. Also, we used the gridsearch method to select dropout hyper-parameters, learning rate, and the number of GRU units, and the Akaike Information Criterion (AIC) criterion confirmed the results of this proposed combination. The results show that the proposed model in the aspect-based sentiment analysis section has been able to achieve 5.94% accuracy and 9.9% improvement in the f1-score on the SemEval 2015 dataset and 2.61% improvement on the SemEval 2016 dataset in f1-score compared to the state-of-arts. Also, the results of predicting the price trend of cryptocurrencies show that the proposed model has correctly recognized the price trend in the next five days in 77% of cases according to the ROC-AUC criterion.INDEX TERMS Aspect Based sentiment analysis, Prediction trend price, Cryptocurrencies, Pre-trained networks, Hybrid deep learning models.

Development of Context-Based Sentiment Classification for Intelligent Stock Market Prediction

“…This observation directly challenges the notion that public sentiment has a stochastic effect on stock prices, underscoring the hidden influence of extreme sentiments on market dynamics. Further diverging from past findings [41,42], we posited that Twitter influencers might have significant sway over market movements, a hypothesis not extensively explored in prior work [43]. We explored sentiment fluctuations over time, adopting an hourly classification to uncover potential impacts on stock performance.…”

Development of Context-based Sentiment Classification for Intelligent Stock Market Prediction

“…This observation directly challenges the notion that public sentiment has a stochastic effect on stock prices, underscoring the hidden influence of extreme sentiments on market dynamics. Further diverging from past findings [38][39], we posited that Twitter influencers might have significant sway over market movements, a hypothesis not extensively explored in prior work [40]. We explored sentiment fluctuations over time, adopting an hourly classification to uncover potential impacts on stock performance.…”

RMDEASD: Integrating Rule Mining and Deep Learning for Enhanced Aspect-Based Sentiment Analysis Across Diverse Domains

“…Lin et al [6] proposed a contrastive learning approach for cross-lingual ABSA, indicating the growing need for models that can perform sentiment analysis across different languages. In the realm of transfer learning, Jahanbin and Chahooki [10] utilized hybrid deep transfer learning models to analyze sentiments of Twitter influencers, a method that has shown promise in enhancing the adaptability of ABSA models to different domains. Similarly, Zhang et al [7] developed an Efficient Adaptive Transfer Network (EATN) for aspect-level sentiment analysis, emphasizing the effectiveness of transfer learning in ABSA.…”