Sentiment Analysis of Twitter Data

Wang, Yili; Guo, Jianhua; Yuan, Chengsheng; Li, Baozhu

doi:10.3390/app122211775

Cited by 29 publications

(20 citation statements)

References 75 publications

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“…Public sentiment often plays a crucial role in determining asset prices in financial markets, and this effect is pronounced in cryptocurrencies. Social media platforms, particularly Twitter, serve as primary conduits for public sentiments, making them invaluable sources of data for sentiment analyses 47 – 49 .…”

Section: Data Preparationmentioning

confidence: 99%

Multi-level deep Q-networks for Bitcoin trading strategies

Otabek,

Choi

2024

Sci Rep

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The Bitcoin market has experienced unprecedented growth, attracting financial traders seeking to capitalize on its potential. As the most widely recognized digital currency, Bitcoin holds a crucial position in the global financial landscape, shaping the overall cryptocurrency ecosystem and driving innovation in financial technology. Despite the use of technical analysis and machine learning, devising successful Bitcoin trading strategies remains a challenge. Recently, deep reinforcement learning algorithms have shown promise in tackling complex problems, including profitable trading strategy development. However, existing studies have not adequately addressed the simultaneous consideration of three critical factors: gaining high profits, lowering the level of risk, and maintaining a high number of active trades. In this study, we propose a multi-level deep Q-network (M-DQN) that leverages historical Bitcoin price data and Twitter sentiment analysis. In addition, an innovative preprocessing pipeline is introduced to extract valuable insights from the data, which are then input into the M-DQN model. A novel reward function is further developed to encourage the M-DQN model to focus on these three factors, thereby filling the gap left by previous studies. By integrating the proposed preprocessing technique with the novel reward function and DQN, we aim to optimize trading decisions in the Bitcoin market. In the experiments, this integration led to a noteworthy 29.93% increase in investment value from the initial amount and a Sharpe Ratio in excess of 2.7 in measuring risk-adjusted return. This performance significantly surpasses that of the state-of-the-art studies aiming to develop an efficient Bitcoin trading strategy. Therefore, the proposed method makes a valuable contribution to the field of Bitcoin trading and financial technology.

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Section: Data Preparationmentioning

confidence: 99%

Multi-level deep Q-networks for Bitcoin trading strategies

Otabek,

Choi

2024

Sci Rep

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“…[12] presents a method for sentiment analysis of drug reviews using a combination of a lexicon-based approach and a deep learning model. Twitter Sentiment Analysis Operational Flow [14] The lexicon generation is modular, and the sentiment analysis is performed using a fusion of a convolutional neural network (CNN) and a recurrent neural network (RNN) with threshold weighted mapping. The approach is evaluated on a dataset of drug reviews, and the results demonstrate improved sentiment analysis compared to other state-of-the-art methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed method is evaluated on a dataset of historical stock prices, and the results show improved performance compared to other state-of-the-art models. Sentiment Analysis of Twitter Data by [14] presents a study of the performance of CNNs and LSTMs on sentiment analysis of Twitter data, and compares their results with traditional machine learning approaches. This is also shown in figure 1.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Convolutional Network and Long Short-Term Memory (HBCNLS) model for Sentiment Analysis on Movie Reviews

Dubey¹,

Khera

Grover

et al. 2023

IJRITCC

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This paper proposes a hybrid model (HBCNLS) for sentiment analysis that combines the strengths of multiple machine learning approaches. The model consists of a convolutional neural network (CNN) for feature extraction, a long short-term memory (LSTM) network for capturing sequential dependencies, and a fully connected layer for classification on movie review dataset. We evaluate the performance of the HBCNLS on the IMDb movie review dataset and compare it to other state-of-the-art models, including BERT. Our results show that the hybrid model outperforms the other models in terms of accuracy, precision, and recall, demonstrating the effectiveness of the hybrid approach. The research work also compares the performance of BERT, a pre-trained transformer model, with long short-term memory (LSTM) networks and convolutional neural networks (CNNs) for the task of sentiment analysis on a movie review dataset..

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“…Sentiment analysis (SA) is also called Opinion analysis or Opinion mining, it is a subfield of natural language processing (NLP) that evaluates the degree of polarity in the sentence to analyze and extracts feelings from text data. Several types of research have been carried out by establishments for finding people's sense of a given matter [5]- [6]. The polarity involves determining the attitude or emotion expressed in a piece of text, which can be positive, negative, or neutral.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

Ahmad¹,

Umar²

2023

IJCAI

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Recently, extensive research in the field of financial sentiment analysis has been conducted. Sentiment analysis (SA) of any text data denotes the feelings and attitudes of the individual on particular topics or products. It applies statistical approaches with artificial intelligence (AI) algorithms to extract substantial knowledge from a huge amount of data. This study extracts the Sentiment polarity (negative, positive, and neutral) from financial textual data using machine learning and deep learning algorithms. The constructed machine learning model used Multinomial Naïve Bayes (MNB) and Logistic regression (LR) classifiers. On the other hand, three deep learning algorithms have been utilized which are Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results of the MNB and LR obtained a good and very good rate of accuracy respectively. Likewise, the results of RNN, LSTM, and GRU obtained an excellent rate of accuracy. It can be concluded from the outcomes that the used preprocessing stages made a positive impact on the accuracy rate. Povzetek: Raziskava analizira finančna besedila z uporabo klasičnega strojnega učenja in globokega učenja z namenom iskanja sentimenta (negativno, pozitivno, nevtralno).

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Sentiment Analysis of Twitter Data

Cited by 29 publications

References 75 publications

Multi-level deep Q-networks for Bitcoin trading strategies

Multi-level deep Q-networks for Bitcoin trading strategies

A Hybrid Convolutional Network and Long Short-Term Memory (HBCNLS) model for Sentiment Analysis on Movie Reviews

Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

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