do a gap analysis based on the litrature review below


1. Sentiment Analysis: Sentiment analysis is a technique used to detect subjective opinions or emotions in textual data, like social media posts and product reviews. Several studies propose novel techniques and models to improve sentiment analysis in different applications. For example, Huang et al. (2023) present an aspect-based sentiment analysis model using contrastive learning and adversarial training for POI recommendations. In another work, Alotaibi et al. (2023) employ deep learning techniques like RNN and LSTM to classify people’s opinions about COVID-19 vaccines based on Twitter data. These studies highlight the importance of sentiment analysis in understanding user preferences and opinions. In the study by Jassim et al. (2023), the authors compare machine learning (ML) classifiers such as decision trees, support vector machines, and ensemble methods for sentiment analysis of movie reviews. They found that the support vector machine classifier yielded the best precision rate (88.33%). This demonstrates the effectiveness of machine learning classifiers in sentiment analysis tasks. In the study by Zeng et al. (2023), the authors proposed a semantic heterogeneous graph convolutional network for aspect-level sentiment analysis. Their findings revealed improved performance compared to baseline methods. Similarly, Kaur and Sharma (2023) developed a hybrid feature extraction approach for consumer sentiment analysis with a deep learning-based model, yielding high average precision, recall, and F1-scores on three different datasets.

Natural Language Processing (NLP): NLP is the field of artificial intelligence focused on the interaction between computers and humans using natural language. In Yang et al.'s (2023) study, they use NLP techniques to locate bug code snippets in Java and Android projects by analyzing controversial sentences from StackOverflow comments. Their results demonstrate that NLP can be effective in understanding complex and loosely organized writing by ordinary developers, thus contributing to the field of software security. Kora et al. (2023) proposed a meta-ensemble deep learning approach for sentiment analysis, which combines the power of ensemble learning and deep learning. They conducted experiments on six benchmark datasets in different languages and dialects, showcasing improved performance compared to baseline deep learning models. In Başarslan and Kayaalp’s (2023) work, they proposed a novel Multi Bi-GRU and Multi CNN-Based deep learning model (MBi-GRUMCONV) for social media sentiment analysis. Additionally, Benrouba et al. (2023) explored using NLP techniques such as WordNet semantic relations to enrich the KNN distance computation and handle implicit aspect identification tasks.


BERT: BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained NLP model for contextualized word embeddings. It has been widely used for various tasks, including sentiment analysis and text classification. In the study by Zou and Wang (2023), they propose a semi-supervised short text sentiment classification method based on an improved BERT model utilizing unlabelled data. Their findings show that BERT can accurately capture the semantics of short texts and improve sentiment analysis performance. Mohamed et al. (2023) employed BERT along with ensemble learning to construct a high-performance model for Arabic sentiment analysis that addresses the Arabic language’s complexities and imbalanced data issues.

Deep Learning: Deep learning techniques have become popular for various NLP and sentiment analysis tasks, as they can automatically learn complex representations of text data. Fadhli et al. (2023) propose a deep learning-based credibility conversation detection approach for social networks, using post and user features along with multi-dense layers for better representation. Their approach demonstrates that deep learning methods can effectively analyze conversation credibility on social media platforms. AminiMotlagh et al. (2023) used four widely recognized data mining classifiers, including support vector machines and decision trees, for sentiment analysis of tweets. They found that support vector machines achieved the highest classification accuracy among the classifiers used. In the study by Benarafa et al. (2023), the authors proposed an emotional sentiment analysis model for mental health safety using deep learning, which demonstrates the versatility of deep learning techniques for various applications.

Social Media Analysis: Social media data contains valuable information about user preferences and opinions, making it an important source for sentiment analysis and NLP tasks. In the study by Rita et al. (2023), they analyze tweets during an electoral period to understand the impact of social media on voting decisions. Their findings suggest that while sentiment analysis of social media content provides insight into public opinion, it may not be an accurate predictor of election outcomes. In the paper by Qaqish et al. (2023), the authors analyzed the sentiment of tweets on the post-COVID transition to hybrid learning in the Jordanian community. Their findings revealed that 18.75% of the studied sample expressed dissatisfaction, suggesting the importance of understanding and interpreting user sentiments in social networks.

Data Mining: Data mining refers to the process of discovering patterns and extracting valuable information from large datasets. In the work of Qaqish et al. (2023), the authors used data mining classifiers, such as k-nearest neighbor, decision tree, and naive Bayes, to analyze the sentiment of tweets from the Arabic-Egyptian Corpus 2 dataset. They reported that deep learning models coupled with ensemble learning improved the overall sentiment classification performance.


Ensemble Learning: Ensemble learning combines multiple learning algorithms to create a more robust model with better performance than any single algorithm. In the study by AminiMotlagh et al. (2023), the authors utilized ensemble techniques to decrease variance and bias in their learning algorithms, resulting in increased reliability of their single classifiers. Verma et al. (2022) employed an NLP-based algorithm to perform opinion mining on Twitter data and performed ensemble learning to analyze and evaluate the public sentiment towards COVID-19 vaccines in India. Mohamed et al. (2023) proposed an ensemble learning approach that combines multiple transformer-based models, such as XLM-T and MARBERT, to tackle the challenges faced in Arabic sentiment analysis. Their results show that the ensemble approach outperforms individual state-of-the-art models on all tested datasets.

	
Opinion Mining: Opinion mining is a subfield of sentiment analysis that focuses on extracting and analyzing sentiments expressed in textual data. In the work of Qaqish et al. (2023), the authors proposed an approach to filter emotionally harmful social media content using NLP techniques and IBM API, showcasing the importance of mining and understanding opinions in social media content.

Social Networks Analysis: Social networks are rich sources of user opinions and preferences, making them important for sentiment analysis tasks. In the paper by Benarafa et al. (2023), the authors analyzed social media content for mental health safety using Deep Learning techniques. Similarly, Kora et al. (2023) explored sentiment analysis and emotion detection of post-COVID educational tweets, offering valuable insights into the Jordanian community’s perspectives on hybrid learning.



Long Short-Term Memory (LSTM):
LSTM models have been widely adopted in various Sentiment Analysis applications due to their ability to efficiently learn and remember long-range dependencies in data. Ayyappa et al. (2023) proposed an optimized LSTM-based stock price prediction model that utilizes sentiment scores computed from news data. The model integrates text features extracted using Bag of words, n-Gram, and TFIDF with technical indicator-based features from stock data. The optimal LSTM training process is tuned by employing a combination of Harris Hawks Optimization Algorithm and Sparrow Search Algorithm.

Optimized Models:
The use of optimization algorithms to enhance the performance of sentiment analysis and text classification models has gained significant attention. For example, Ayyappa et al. (2023) utilized Harris Hawks Induced Sparrow Search Optimization to discover the optimal weights for their LSTM-based model, resulting in improved stock price prediction performance. This demonstrates the potential of optimized models for improved performance in various text analysis tasks.

Arabic Text:
Sentiment analysis in Arabic text presents a unique set of challenges due to the language’s complex morphology, dialects, and limited resources. Mohamed et al. (2023) proposed an ensemble transformer-based model combining a multilingual transformer model (XLM-T) and a monolingual model (MARBERT) for Arabic sentiment analysis. They demonstrated that the ensemble model outperforms state-of-the-art models on multiple datasets, including ASAD, ArSarcasm-v2, and SemEval-2017. This highlights the importance of adapting sentiment analysis techniques to cater to the intricacies of different languages.

Perceptible Analysis, Valence, Arousal, and Dominance:
Perceptible sentiment analysis aims to classify emotions in texts along multiple dimensions, such as valence (attraction), arousal (level of activation), and dominance (level of control). Roy et al. (2023) presented a novel approach to analyze student sentiments within WhatsApp group chats in the valence, arousal, and dominance space. The proposed method used a Support Vector Machine (SVM) model fused with a language classifier to calculate sentiment ratings for each message and classify them according to the VAD scale.

Machine Learning:
Machine learning techniques have been widely applied to sentiment analysis and text classification tasks. For instance, Roy et al. (2023) utilized machine learning methods like SVM to classify and analyze WhatsApp messages according to the valence, arousal, and dominance scale. The success of machine learning techniques in these tasks shows their position as a key component in constructing efficient sentiment analysis models.

WhatsApp Analysis:
WhatsApp, being a popular messaging platform, presents an opportunity for sentiment analysis and communication analysis. Roy et al. (2023) performed sentiment analysis on students’ WhatsApp group chats and found that groups were mainly used for sharing information, exchanging ideas, and discussing issues. This demonstrates the potential for exploring sentiment analysis in diverse communication platforms like WhatsApp.