GRUvader: Sentiment-Informed Stock Market Prediction DOI Creative Commons

Akhila Mamillapalli,

Bayode Ogunleye,

Sonia Timoteo Inacio

и другие.

Mathematics, Год журнала: 2024, Номер 12(23), С. 3801 - 3801

Опубликована: Ноя. 30, 2024

Stock price prediction is challenging due to global economic instability, high volatility, and the complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market further examined influence a sentiment analysis indicator on prices. Our results were two-fold. Firstly, we used lexicon-based approach identify features, thus evidencing correlation between movement. Secondly, proposed use GRUvader, an optimal gated recurrent unit network, prediction. findings suggest that stand-alone models struggled with AI-enhanced models. Thus, our paper makes recommendations latter systems.

Язык: Английский

Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection DOI Open Access
Bayode Ogunleye, Hemlata Sharma, Olamilekan Shobayo

и другие.

Опубликована: Июль 16, 2024

The world health organisation (WHO) revealed approximately 280 million people in the suffer from depression. Yet, existing studies on early-stage depression detection using machine learning (ML) techniques are limited. Prior have applied a single stand-alone algorithm which unable to deal with data complexities, prone overfitting and limited generalisation. To this end, our paper examined performance of several ML algorithms for two benchmark social media datasets (D1 D2). More specifically, we incorporated sentiment indicator improve model performance. Our experimental results showed that sentence bidirectional encoder representations transformers (SBERT) numerical vectors fitted into stacking ensemble achieved comparable F1 scores 69% dataset (D1) 76% (D2). findings suggest utilising indicators as additional feature yields an improved thus, recommend development depressive term corpus future work.

Язык: Английский

Процитировано

4
Electric Vehicle Sentiment Analysis Using Large Language Models DOI Open Access
Hemlata Sharma,

Faiz Ud Din,

Bayode Ogunleye

и другие.

Опубликована: Авг. 9, 2024

Sentiment analysis is a technique used to understand the publics’ opinion towards an event, product, or organization. For example, positive negative attitude electric vehicle (EV) brands. This provides companies with valuable insight about public's of their products and In field natural language processing (NLP), transformer models have shown great performances over traditional machine learning algorithms. However, these not been explored extensively in EV domain. are becoming signif-icant competitors automotive industry projected cover up 30% United States light market by 2030 [1]. this study, we present comparative study large (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach generalized autoregressive pretraining for un-derstanding using Lucid motors Tesla YouTube datasets. Results evidenced LLMs like her variants off-the-shelf algorithms sentiment analysis, specifically, when fi-ne-tuned. Furthermore, our findings presents need domain adaptation whilst utilizing LLMs. Finally, experimental results showed that RoBERTa achieved consistent performance across datasets F1 score at least 92%.

Язык: Английский

Процитировано

1
Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection DOI Creative Commons
Bayode Ogunleye, Hemlata Sharma, Olamilekan Shobayo

и другие.

Big Data and Cognitive Computing, Год журнала: 2024, Номер 8(9), С. 112 - 112

Опубликована: Сен. 5, 2024

The World Health Organisation (WHO) revealed approximately 280 million people in the world suffer from depression. Yet, existing studies on early-stage depression detection using machine learning (ML) techniques are limited. Prior have applied a single stand-alone algorithm, which is unable to deal with data complexities, prone overfitting, and limited generalization. To this end, our paper examined performance of several ML algorithms for two benchmark social media datasets (D1 D2). More specifically, we incorporated sentiment indicators improve model performance. Our experimental results showed that sentence bidirectional encoder representations transformers (SBERT) numerical vectors fitted into stacking ensemble achieved comparable F1 scores 69% dataset (D1) 76% (D2). findings suggest utilizing as an additional feature yields improved performance, thus, recommend development depressive term corpus future work.

Язык: Английский

Процитировано

1
GRUvader: Sentiment-Informed Stock Market Prediction DOI Creative Commons

Akhila Mamillapalli,

Bayode Ogunleye,

Sonia Timoteo Inacio

и другие.

Mathematics, Год журнала: 2024, Номер 12(23), С. 3801 - 3801

Опубликована: Ноя. 30, 2024

Stock price prediction is challenging due to global economic instability, high volatility, and the complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market further examined influence a sentiment analysis indicator on prices. Our results were two-fold. Firstly, we used lexicon-based approach identify features, thus evidencing correlation between movement. Secondly, proposed use GRUvader, an optimal gated recurrent unit network, prediction. findings suggest that stand-alone models struggled with AI-enhanced models. Thus, our paper makes recommendations latter systems.

Язык: Английский

Процитировано

0