Sentiment analysis using deep learning techniques: a comprehensive review
With the exponential growth of social media platforms and online communication, the
necessity of using automated sentiment analysis techniques has significantly increased …
necessity of using automated sentiment analysis techniques has significantly increased …
Advancing fake news detection: hybrid deep learning with fasttext and explainable AI
The widespread propagation of misinformation on social media platforms poses a significant
concern, prompting substantial endeavors within the research community to develop robust …
concern, prompting substantial endeavors within the research community to develop robust …
ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media
The emergence of social media has given rise to a variety of networking and communication
opportunities, as well as the well-known issue of cyberbullying, which is continuously on the …
opportunities, as well as the well-known issue of cyberbullying, which is continuously on the …
[HTML][HTML] A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India
In recent years, there has been a growing interest in using artificial intelligence (AI) for
rainfall-runoff modelling, as it has shown promising adaptability in this context. The current …
rainfall-runoff modelling, as it has shown promising adaptability in this context. The current …
Multi-class hate speech detection in the Norwegian language using FAST-RNN and multilingual fine-tuned transformers
E Hashmi, SY Yayilgan - Complex & Intelligent Systems, 2024 - Springer
The growth of social networks has provided a platform for individuals with prejudiced views,
allowing them to spread hate speech and target others based on their gender, ethnicity …
allowing them to spread hate speech and target others based on their gender, ethnicity …
An experimental study of sentiment classification using deep-based models with various word embedding techniques
Nowadays, sentiment analysis is concerned with identifying and analysing text sentiment.
Sentiment analysis has been used in many fields because of its applications in various …
Sentiment analysis has been used in many fields because of its applications in various …
An approach based on semantic relationship embeddings for text classification
Semantic relationships between words provide relevant information about the whole idea in
the texts. Existing embedding representation models characterize each word as a vector of …
the texts. Existing embedding representation models characterize each word as a vector of …
Leveraging attention layer in improving deep learning models performance for sentiment analysis
MY Salmony, AR Faridi, F Masood - International Journal of Information …, 2023 - Springer
Sentiment analysis (SA) is a rapidly expanding research field, making it difficult to keep up
with all of its activities. It aims to examine people's feelings about events and individuals as …
with all of its activities. It aims to examine people's feelings about events and individuals as …
[HTML][HTML] A novel approach for explicit song lyrics detection using machine and deep ensemble learning models
The content of music is not always suitable for all ages. Industries that manage music
content are looking for ways to help adults determine what is appropriate for children. Lyrics …
content are looking for ways to help adults determine what is appropriate for children. Lyrics …
Sentiment analysis of COP9-related tweets: a comparative study of pre-trained models and traditional techniques
S Elmitwalli, J Mehegan - Frontiers in big Data, 2024 - frontiersin.org
Introduction Sentiment analysis has become a crucial area of research in natural language
processing in recent years. The study aims to compare the performance of various sentiment …
processing in recent years. The study aims to compare the performance of various sentiment …