LSTM Based Sentiment Analysis for Cryptocurrency Prediction

Huang, Xin; Zhang, Wenbin; Tang, Xuejiao; Zhang, Mingli; Surbiryala, Jayachander; Iosifidis, Vasileios; Zhang, Ji

doi:10.1007/978-3-030-73200-4_47

Cited by 47 publications

(14 citation statements)

References 2 publications

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“…Bayes (Goel et al 2016), LSTM (Huang et al 2021), BI-LSTM (Lin et al 2021), XLNet (Sweidan et al 2021), BERT , RoBERTa (Liao et al 2021), ULMFiT (Kulkarni et al 2021), ELMo (Nurifan et al 2019), and Albert (Ding et al 2021) to classif y text polarity. The methods perform well and are also reliable (Bhargava & Rao, 2018;Singh & Goel, 2019).…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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Banking is an essential component of our day-to-day activity, and the sector contributes to the development of every Nation's economy. To ensure bank stability, the banks have gone through several deregulation and reformation processes. Unfortunately, these processes and technological advancement have led to increased competitiveness, saturated market, and low profitability. Thus, the need for banks to build customer centric service to gain profitability and stability is vital. This opens the need to investigate customers attitude towards banking.With the growing usage of social network sites (SNS), the user generated content (UGC) has opened opportunities for banks to mine customers opinion . This can help the banks to generate insight into their product and services, create marketing strategies and manage their reputation. To the customers, this serves as source to information that can help in supporting decision making on product or service purchase. In this study, sentiment analysis (SA) techniques were employed to investigate customers attitude towards banking us ing Twitter data. However, the unstructured nature of the data and word ambiguity made sentiment analysis complicated. In the context of this study, SA is more difficult because it involves the natural language processing of Pidgin English and English words in the bank domain.Unfortunately, there are limited or no resources for this purpose.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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“…Everybody wants to grow their money by investing in the stock market, but with the growing technology and the introduction of e-money what can be a better way to make your money grow by investing in crypto currency? (21) Bitcoin or any other crypto currency is not under the influence of any country or government, for this reason, it can be invested by anyone around the globe without the fear of being imposed of taxes from other countries. (7) The success of Bitcoin is measured by its huge capitalism growth and price, it leads to the emerging of various other crypto currencies which differ from Bitcoin in just a few parameters.…”

Section: Problem Statementmentioning

confidence: 99%

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Ho¹,

Vatambeti²,

Ravichandran³

2021

IJST

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Objective: This paper explains the working of the linear regression and Long Short-Term Memory model in predicting the value of a Bitcoin. Due to its raising popularity, Bitcoin has become like an investment and works on the Block chain technology which also gave raise to other crypto currency. This makes it very difficult to predict its value and hence with the help of Machine Learning Algorithm and Artificial Neural Network Model this predictor is tested. Methodology: In this study, we have used data sets for Bitcoin for testing and training the ML and AI model. With the help of python libraries, the data filtration process was done. Python has provided with a best feature for data analysis and visualization. After the understanding of the data, we trim the data and use the features or attributes best suited for the model. Implementation of the model is done and the result is recorded. Finding: It was discovered that the linear regression model's accuracy rate is very high when compared to other Machine Learning models from related works; it was found to be 99.87 percent accurate. The LSTM model, on the other hand, shows a mini error rate of 0.08 percent. This, in turn, demonstrates that the neural network model is more optimized than the machine learning model. Novelty: In this work, a small GUI has been created using the tkinter library that will allow the user to input the High, Low, and Open features values and then predict the next value for the coin. This paper compares the prediction outcomes of a machine learning model and an artificial neural network model. Because linear regression provided the highest accuracy compared to the other machine learning models, we used it to compare it to the LSTM model.

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“…There are many different ways to do sentiment analysis [39]. There are three types of solutions to perform Sentiment Analysis; Rule-Based [40], Feature-Based [41], and Embedding-Based methods [42,43]. The Aspect-Based sentiment analysis model is implemented using the Rule-Based models to measure the input text's subjectivity, indicating if a tweet is a fact or an opinion, and Transformer-Based sentiment analysis for understanding if a tweet induces positive or negative emotions.…”

Section: Aspect-based Sentiment Analysismentioning

confidence: 99%

AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

Shoeibi¹,

Hernández²,

Chamoso³

et al. 2021

Preprint

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Maintaining a healthy cyber society is a big challenge due to the users’ freedom of expression and behaving. It can be solved by monitoring and analyzing the users’ behavior and taking proper actions towards them. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using Graph Analysis methods. Then the users’ behavioral patterns are analyzed by applying Metadata Analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then in the Abnormal Behavior Detection Filtering component, the interesting profiles are selected for further examinations. Finally, in the Contextual Analysis component, the contents will be analyzed using natural language processing techniques; A binary text classification model (SVM + TF-IDF with 88.89% accuracy) for detecting if the tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN with 80% accuracy); because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (Police) suggestions to control hate speech or terrorist propaganda.

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LSTM Based Sentiment Analysis for Cryptocurrency Prediction

Cited by 47 publications

References 2 publications

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

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