HyVADRF: Hybrid VADER–Random Forest and GWO for Bitcoin Tweet Sentiment Analysis

Abstract

In recent years, Bitcoin and other cryptocurrencies have been increasingly considered an investment option for emerging markets. However, its erratic behavior has discouraged some potential investors. To get insights into its behavior and price fluctuation, past studies have discovered the correlation between Twitter sentiments and Bitcoin behavior. Most of them have focused exclusively on their relationships, instead of the Twitter sentiment analysis itself. Finding the most suitable classification algorithms for sentiment analysis for this kind of data is challenging. For enormous data of Twitter, unlabeled data can be time-consuming and expensive for the supervised sentiment analysis approach, which has been studied to be superior to unsupervised ones. As such, we propose HyVADRF: Hybrid VADER -Random Forest and Grey Wolf Optimizer Model. Semantic and rule-based VADER was used to calculate polarity scores and classify sentiments, which overcame the weakness of manual labeling, while Random Forest was utilized as its supervised classifier. Furthermore, considering Twitter's massive size, we collected over 3.6 million tweets and analyzed various dataset sizes as these are related to the model's learning process. Lastly, Grey Wolf Optimizer parameter tuning was conducted to optimize the classifier's performance. The results show that 1) HyVADRF Model returned an accuracy of 75.29 %, precision of 70.22%, recall of 87.70%, and F1-score of 78%. 2) The most ideal percentage of dataset size is 90% of the total collected tweets (n=1,249,060). 3) With standard deviations of 0.0008 for accuracy and F1-score and 0.0011 for precision and recall. Hence, HyVADRF Model consistently delivers stable results.

Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

“…VADER has been used to annotate different text-based dataset, especially tweets. Existing research shows that tweets dataset annotated by VADER produces best result when trained on traditional supervised machine learning [29], [30], [31]. The optimum performance of VADER dataset is achieved from the multiclassification of the dataset [32].…”

HyBiLSTM: Multivariate Bitcoin Price Forecasting Using Hybrid Time-Series Models With Bidirectional LSTM

“…While BIC algorithms may require significant time and resources due to parameter optimization and iterative processes, they possess the ability to discover unknown patterns and rely less on mathematical modelling or exhaustive training [29]. Based on the social structure and hunting nature of grey wolves, GWO was selected among BIC algorithms because of its past performance in varied domains and simplicity [30], [31], [32]. Holding the highest hierarchy, alpha (α) wolf makes decisions for the pack, followed by the beta (β) wolf acting as the alpha's advisor and enforcer of pack discipline.…”

Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian Language

“…The main contributions of this study include the following: firstly, we propose a novel approach that incorporates negation cues (ne, nećeš, nećete, neću, nema, nemaju, nemam, nemaš, nemate, nemoj, ni, nigdje, nije, nijedan, nijedna, nijedno, nikad, nikada, niko, nisam, nisi, nismo, ništa, niste, nisu, odbijen, poriče, poričem, poričemo, poričeš, poričete), AnAwords, and sentiment-labeled words from a lexicon to enhance the accuracy of sentiment analysis in Bosnian language tweets. Secondly, we explore various machine-learning techniques, including SVM [14][15][16], Naive Bayes [17][18][19], RF [20][21][22], and CNN [23][24][25], optimizing their parameters to achieve optimal performance. Thirdly, we leverage a pretrained model, BOSentiment, to provide an initial sentiment value for each tweet, which is then used as input for the CNN.…”