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What is natural language processing?

6 cited papers · March 16, 2026 · Powered by Researchly AI

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Natural Language Processing (NLP) is a field that sits at the intersection of multiple disciplines and enables computational systems to understand and process h…

Natural Language Processing (NLP) is a field that sits at the intersection of multiple disciplines and enables computational systems to understand and process human language.¹NLP techniques have been applied to tasks such as sentiment analysis, emotion detection, and knowledge extraction from text.²

1

Language Models as Knowledge Bases?Fabio Petroni, Tim Rocktäschel et al.2019OpenAlex

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Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic TextHanane Elfaik, El Habib Nfaoui2020Journal of Intelligent Systems

NLP encompasses a range of approaches including keyword-based methods, lexicon-based methods, and machine learning methods, each with distinct capabilities and limitations. Singh et al. (2024)

Sentiment Analysis — A core NLP application that identifies people's attitudes, sentiments, and emotions towards a given goal, such as people, activities, organizations, services, or products.

¹²³

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Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic TextHanane Elfaik, El Habib Nfaoui2020Journal of Intelligent Systems

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Potential Approach for Text-Based Emotion Detection Using NLP Coupled With Deep Learning of Sentiment AnalysisGurpreet Singh, Deependra Pratap Singh et al.2024OpenAlex

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Text-Based Emotion Recognition Using Deep Learning ApproachSantosh Kumar Bharti, S Varadhaganapathy et al.2022Computational Intelligence and Neuroscience

Emotion Detection — A subset of sentiment analysis that predicts specific emotions rather than broadly categorizing text as positive, negative, or neutral; text-based emotion detection is particularly challenging due to the absence of cues like tonal stress and facial expressions.

²³Singh et al. (2024)²

Frequency Effects in Language — Language processing is intimately tuned to input frequency, with implications for how linguistic patterns are acquired and represented.

⁴Ellis (2002)⁴

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FREQUENCY EFFECTS IN LANGUAGE PROCESSINGNick C. Ellis2002Studies in Second Language Acquisition

Language Models as Knowledge Bases — Pre-trained language models can store and retrieve relational knowledge, functioning similarly to structured knowledge bases. Petroni et al. (2019)

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Diagram

Raw Text Input
 |
 v
[Preprocessing Layer]
 - Tokenization
 - Normalization
 |
 v
[Feature Extraction]
 - Embeddings (word/sentence)
 - Frequency-based features
 |
 v
[NLP Model Layer]
 - Keyword / Lexicon-based
 - Machine Learning (e.g., SVM)
 - Deep Learning (e.g., BiLSTM, CNN, Bi-GRU)
 |
 v
[Output Layer]
 - Sentiment / Emotion Label
 - Knowledge / Information Extracted

NLP techniques can be broadly categorized into three approaches: keyword-based, lexicon-based, and machine learning-based.¹However, keyword- and lexicon-based strategies have notable limitations, particularly in handling semantic relations. Singh et al. (2024)²

1

Language Models as Knowledge Bases?Fabio Petroni, Tim Rocktäschel et al.2019OpenAlex

2

Potential Approach for Text-Based Emotion Detection Using NLP Coupled With Deep Learning of Sentiment AnalysisGurpreet Singh, Deependra Pratap Singh et al.2024OpenAlex

To overcome these limitations, hybrid models combining machine learning and deep learning have been proposed.

Deep learning models such as Bidirectional LSTM (BiLSTM) have shown great success in NLP tasks like Arabic sentiment analysis by capturing contextual information from feature sequences in both forward and backward directions.³Elfaik & Nfaoui (2020)³

3

Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic TextHanane Elfaik, El Habib Nfaoui2020Journal of Intelligent Systems

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Keyword- and lexicon-based NLP approaches have limitations in handling semantic relations, making them less effective for nuanced language understanding tasks.

¹

1

Potential Approach for Text-Based Emotion Detection Using NLP Coupled With Deep Learning of Sentiment AnalysisGurpreet Singh, Deependra Pratap Singh et al.2024OpenAlex

Arabic NLP, as one example domain, faces particular challenges including morphological richness, various dialects, scarcity of resources, and the absence of explicit sentiment words in implicit text.

²

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Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic TextHanane Elfaik, El Habib Nfaoui2020Journal of Intelligent Systems

NLP lies at the intersection of fields such as computational linguistics, data mining, and machine learning.

¹

Language processing is deeply tuned to input frequency, which shapes how linguistic patterns are learned and represented.

²

Pre-trained language models can serve as implicit knowledge bases, storing relational knowledge without explicit structured databases.

³

Hybrid deep learning models outperform traditional keyword and lexicon approaches for emotion and sentiment detection in text.

⁴⁵

Dialogue and interactive language use present unique processing challenges that monologue-based accounts of language may not fully address.

²

1

Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic TextHanane Elfaik, El Habib Nfaoui2020Journal of Intelligent Systems

2

FREQUENCY EFFECTS IN LANGUAGE PROCESSINGNick C. Ellis2002Studies in Second Language Acquisition

3

Language Models as Knowledge Bases?Fabio Petroni, Tim Rocktäschel et al.2019OpenAlex

4

Potential Approach for Text-Based Emotion Detection Using NLP Coupled With Deep Learning of Sentiment AnalysisGurpreet Singh, Deependra Pratap Singh et al.2024OpenAlex

5

Text-Based Emotion Recognition Using Deep Learning ApproachSantosh Kumar Bharti, S Varadhaganapathy et al.2022Computational Intelligence and Neuroscience

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"Deep learning architectures for natural language processing tasks: a survey"
"Transformer-based models for sentiment analysis and emotion detection"
"Pre-trained language models for knowledge extraction and question answering"

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