☆ 4.6 Article

Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model

IEEE ACCESS (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Artificial Intelligence

Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model

Ramish Jamil et al.

Summary: Sarcasm is prevalent on social networking sites, making it challenging to detect due to the use of positive vocabulary for negative thoughts. This study introduces a hybrid approach combining CNN and LSTM for sarcasm detection on a multi-domain dataset, outperforming traditional machine learning algorithms with an accuracy of 91.60%. The proposed model is accurate, robust, and performs well in sarcasm detection compared to state-of-the-art approaches.

PEERJ COMPUTER SCIENCE (2021)

添加到收藏夹

Article Multidisciplinary Sciences

A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis

Furqan Rustam et al.

Summary: This research conducts sentiment analysis on Covid-19 tweets using a supervised machine learning approach, proposes a feature set, evaluates the performance of various machine learning classifiers, and investigates the use of the Long Short-Term Memory (LSTM) architecture of the deep learning model. Results indicate that Extra Trees Classifiers outperform all other models in accuracy.

PLOS ONE (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Deepfake tweets classification using stacked Bi-LSTM and words embedding

Vaibhav Rupapara et al.

Summary: This paper introduces a deep learning model to predict the sentiment of deep fake tweets, which outperforms other machine learning and deep learning models with an accuracy of 0.92.

PEERJ COMPUTER SCIENCE (2021)

添加到收藏夹

Article Chemistry, Multidisciplinary

Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19

Muhammad Mujahid et al.

Summary: Amid the COVID-19 pandemic, the closure of educational institutes has led to a significant rise in online learning. Concerns about the effectiveness of e-learning are addressed in this study through sentiment analysis on social media platforms. Machine learning models and deep learning approaches have been utilized to analyze sentiment and evaluate the performance of e-learning.

APPLIED SCIENCES-BASEL (2021)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

LSTM Based Sentiment Analysis for Cryptocurrency Prediction

Xin Huang et al.

Summary: This research focuses on predicting the volatile price movement of cryptocurrency by analyzing sentiment in social media and finding the correlation between them. The proposed approach using LSTM-based recurrent neural network outperforms the state of the art auto regressive based model by 18.5% in precision and 15.4% in recall.

DATABASE SYSTEMS FOR ADVANCED APPLICATIONS (DASFAA 2021), PT III (2021)

添加到收藏夹

Article Computer Science, Information Systems

A ConvBiLSTM Deep Learning Model-Based Approach for Twitter Sentiment Classification

Sakirin Tam et al.

Summary: The article introduces the research status and methods of Twitter sentiment analysis, pointing out that using CNN or Bi-LSTM alone for sentiment analysis cannot achieve optimal results. It proposes a combination of CNN and Bi-LSTM ConvBiLSTM model, which has been successfully applied to Twitter datasets.

IEEE ACCESS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multilayer probability extreme learning machine for device-free localization

Jie Zhang et al.

NEUROCOMPUTING (2020)

添加到收藏夹

Article Automation & Control Systems

Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines

Jie Zhang et al.

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS (2020)

添加到收藏夹

Article Computer Science, Information Systems

Emotion Detection in Online Social Networks: A Multilabel Learning Approach

Xiao Zhang et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

添加到收藏夹

Article Management

Emotions in Twitter communication and stock prices of firms: the impact of Covid-19 pandemic

Suparna Dhar et al.

Summary: This study investigated the impact of emotional expression in social media communication on stock prices of firms, especially in the context of crisis. The findings revealed that emotional content in organizational tweets significantly predicted stock prices, with a moderation effect of crisis on this association. The study provides insights for practitioners and regulators to enhance organizational communication during crises and leverage Twitter for effective crisis response.

DECISION (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Text emotion detection in social networks using a novel ensemble classifier based on Parzen Tree Estimator (TPE)

Fereshteh Ghanbari-Adivi et al.

NEURAL COMPUTING & APPLICATIONS (2019)

添加到收藏夹

Article Physics, Multidisciplinary

Tweets Classification on the Base of Sentiments for US Airline Companies

Furqan Rustam et al.

ENTROPY (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Automatic emotion detection in text streams by analyzing Twitter data

Maryam Hasan et al.

INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS (2019)

添加到收藏夹

Article Business, Finance

Cryptocurrency: A New Investment Opportunity?

David Lee Kuo Chuen et al.

JOURNAL OF ALTERNATIVE INVESTMENTS (2018)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Class-specific cost regulation extreme learning machine for imbalanced classification

Wendong Xiao et al.

NEUROCOMPUTING (2017)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.