Artificial Intelligence (AI) is a transformative technology that is reshaping industries by automating tasks, optimizing processes, and extracting insights from vast amounts of data. One of its branches is Natural Language Processing (NLP), which bridges the gap between human communication and machine interpretation. NLP enables applications such as email filtering, real-time translations, and social media monitoring to detect potential security risks.
Despite its advantages, integrating NLP into industrial applications comes with challenges, including socio-economic concerns regarding job displacement due to automation and data security risks stemming from cyberattacks on training datasets. Addressing these issues requires a careful balance between technological advancements and ethical considerations.
In this study, we explore the application of NLP through text classification models, particularly focusing on sentiment analysis. Our objective is to establish a taxonomy of classification algorithms and develop classifiers for three real-world problems. Additionally, we implement training optimization techniques and data anonymization methods to ensure responsible AI development.
By overcoming technical and ethical challenges in NLP and sentiment analysis, this work to help to make informed decisions regarding the adoption of NLP algorithms.
AI has become one of the most impactful technologies of the 21st century. Beyond automating repetitive tasks, AI is revolutionizing industries by enabling predictive analytics and intelligent decision-making. In healthcare, AI assists in early disease detection and personalized treatment recommendations. In supply chain management, it improves logistics efficiency and demand forecasting. In the financial sector, AI-powered fraud detection has significantly enhanced transaction security.
The concept of AI, however, is not new. The field traces its roots back to the 1950s when Alan Turing posed the question, "Can machines think?" This led to the development of the Turing Test, a foundational concept for evaluating machine intelligence. Over the decades, various machine learning approaches, including neural networks and learning algorithms, have evolved to make AI what it is today—capable of performing tasks once considered science fiction, such as autonomous vehicles and real-time decision-making systems.
Currently, NLP plays a vital role in industrial applications due to the increasing computational power of machines and their ability to analyze massive datasets, including text, images, and audio. However, AI adoption is hindered by factors such as data dependency and social resistance. Automated systems that replace human labor in tasks such as customer service, data entry, and sensor monitoring raise concerns about job security. Additionally, the reliance on vast datasets for model training introduces security risks, as these databases can become targets for cyberattacks, potentially exposing sensitive user information.
Sentiment analysis, a key application of NLP, helps organizations anticipate customer needs and improve decision-making. However, its implementation is met with ethical dilemmas, particularly regarding data privacy. Companies leveraging personal data for marketing strategies must ensure they do not infringe on user rights or manipulate consumer preferences.
Since AI models rely on user-generated data, it is crucial to implement privacy-preserving techniques. The study examines different anonymization methods, including:
- Named Entity Recognition (NER): Identifies and replaces sensitive information (e.g., names, locations) with generalized labels.
- Randomization: Introduces noise into the dataset to obscure sensitive details without compromising analytical integrity.
- Masking: Replaces private data with placeholders, ensuring compliance with data protection regulations such as GDPR.
The study evaluates NLP frameworks based on usability, algorithm support, and industrial adoption. While Python dominates the AI ecosystem with libraries like scikit-learn, TensorFlow, and PyTorch, we also explore industry alternative languages like C# (ML.NET) and Java (Apache OpenNLP) to determine their compatibility with NLP tasks.
Study Details
The study focused on applying NLP techniques to text classification, with an emphasis on sentiment analysis. Our approach involved identifying key data sources, developing a taxonomy of classification algorithms, and testing models in real-world scenarios. To ensure ethical data usage, we explored privacy-preserving methods such as anonymization and data security measures.
Our primary objective was to develop reliable text classification models that could analyze sentiment in different contexts. We identified three specific case studies:
- Restaurant reviews, classifying reviews as positive, neutral, or negative.
- Sarcasm detection, identifying whether a statement is sarcastic or not.
- Cyberbullying detection and categorization of harmful online interactions.
Data privacy was a key concern, so we implemented Named Entity Recognition (NER) to anonymize sensitive user information before processing the data.
We categorized text classification algorithms into four groups based on their strengths:
- Statistical Models: Naïve Bayes, Logistic Regression, and Support Vector Machines (SVM).
- Sequential Models: Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU).
- Tree-Based Models: Decision Trees, Random Forest, and Gradient Boosting (LightGBM).
- Embedding-Based Approaches: Universal Sentence Encoder (USE), Word2Vec, and FastText.
We tested these models across our three case studies, considering factors such as training time, computational efficiency, and accuracy.
Once the models were selected, we trained them using datasets relevant to our case studies. To optimize performance, we applied:
- Hyperparameter Tuning: Adjusting parameters like learning rate, batch size, and number of hidden layers.
- Ensemble Learning: Combining multiple models to improve accuracy and reduce bias.
- Continuous Training Pipelines: Implementing an automated system to periodically retrain models using fresh data.
For cyberbullying detection, ensemble learning proved particularly effective. By aggregating predictions from different models, we achieved a 2-3% increase in classification accuracy compared to using a single model.
Sentiment Analysis Performance
Our sentiment classification models achieved accuracy rates between 80% and 85% on a dataset of 50,000 records. However, sarcasm detection remained a challenge, with an accuracy of only 60% due to the nuanced nature of sarcastic expressions.
Cyberbullying Detection and Bias Reduction
The cyberbullying detection model performed well, accurately identifying harmful interactions 82% of the time. However, we observed that cultural and linguistic variations affected the model’s ability to generalize across different online communities.
Data Privacy and Ethical AI Development
Our anonymization techniques successfully protected user identities without significantly degrading model performance. However, we found that excessive anonymization led to a loss of contextual meaning, reducing classification accuracy.
Improving Sarcasm Detection
The low performance in sarcasm classification suggests the need for context-aware models that consider factors like emojis, punctuation, and tone. Future work could explore transformer-based architectures such as BERT or GPT models to better capture contextual nuances.
This study demonstrated the potential of NLP in text classification, specifically for sentiment analysis and cyberbullying detection. Our findings highlight the importance of model selection, data ethics, and continuous learning in AI-driven decision-making.
