ArticleArtificial intelligence review2023
Impact of word embedding models on text analytics in deep learning environment: a review.
Article in Artificial intelligence review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A standardized naturalistic audio stimulus dataset with unsupervised labeling.Scientific data · 2026Article
- DeepSentRec: a deep learning-based sentiment-aware product recommendation system.Scientific reports · 2026Article
- AI driven dual constraint cooptimization of affective semantics and engineering parameters for biomimetic product design.Scientific reports · 2026Article
- Embeddings from language models are good learners for single-cell data analysis.Patterns (New York, N.Y.) · 2026Article
- Using Data Science to Examine Conflict-Related Sexual Violence.Violence against women · 2025Article
- Tackling toxicity in Arabic social media through advanced detection techniques.Scientific reports · 2025Article
- Representation of Medical Concepts in Emojis Using Medical Subject Headings to Identify Gaps and Opportunities: Cross-Sectional Analysis.JMIR formative research · 2025Article
- scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis.bioRxiv : the preprint server for biology · 2025Article
- Demixer: a probabilistic generative model to delineate different strains of a microbial species in a mixed infection sample.Bioinformatics (Oxford, England) · 2025Article
- Small, Open-Source Text-Embedding Models as Substitutes to OpenAI Models for Gene Analysis.bioRxiv : the preprint server for biology · 2025Article
- fragSMILES as a chemical string notation for advanced fragment and chirality representation.Communications chemistry · 2025Article
- Fast2Vec, a modified model of FastText that enhances semantic analysis in topic evolution.PeerJ. Computer science · 2025Article
- Particle swarm optimization-based NLP methods for optimizing automatic document classification and retrieval.PloS one · 2025Article
- Small, open-source text-embedding models as substitutes to OpenAI models for gene analysis.Computational and structural biotechnology journal · 2025Article
- Leveraging sentiment analysis of food delivery services reviews using deep learning and word embedding.PeerJ. Computer science · 2025Article
- In-depth exploration of software defects and self-admitted technical debt through cutting-edge deep learning techniques.PloS one · 2025Article
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- Enhancing chemical synthesis research with NLP: Word embeddings for chemical reagent identification-A case study on nano-FeCu.iScience · 2024Article
- Novel Approach to Personalized Physician Recommendations Using Semantic Features and Response Metrics: Model Evaluation Study.JMIR human factors · 2024Article
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3 authors.
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Abstract
The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.
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