Social Media Sentiment Intelligence Through NLP and Computational Learning Techniques

Abstract

Social media platforms have grown at an exponential rate leading to the generation of huge amounts of user-generated text (i.e., user-generated content) that can provide valuable information on public opinion, consumer behaviour, and social trends. As a result, sentiment analysis has developed as an important area of research to extract valuable information from this unstructured data. Traditional methods used for performing sentiment analysis often have challenges with context (i.e., trying to understand how a word is being used), sarcasm (i.e., sarcasm is very common in social media), informal language, and multiple languages, all of which are prevalent on social media. This paper presents the development of a hybrid framework that combines the use of advanced Natural Language Processing (NLP) techniques and machine learning algorithms for predicting sentiment in social media. The hybrid framework consists of three stages: text preprocessing, feature extraction from text using TF-IDF and word embeddings, and sentiment classification using a combination of hybrid machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest (RF) and Long Short-Term Memory (LSTM). Sentiment classification is performed using a benchmark set of social media data to classify sentiments into positive, negative and neutral categories. Results from the experiments demonstrate that the proposed hybrid algorithm achieves higher prediction accuracy, precision, recall and F1 score than traditional sentiment analysis techniques.

Introduction

Social media platforms generate massive amounts of unstructured text data, making sentiment analysis an important task in Natural Language Processing (NLP) and AI. The goal is to classify user opinions as positive, negative, or neutral to help businesses, governments, and researchers understand public sentiment and decision-making trends. However, challenges such as slang, emojis, sarcasm, multilingual content, and informal language make accurate sentiment detection difficult.

To address this, the study proposes a hybrid sentiment analysis framework that combines NLP techniques with machine learning and deep learning models. It uses multiple feature extraction methods such as TF-IDF, Bag-of-Words, Word2Vec, GloVe, and transformer-based embeddings like BERT to better capture both syntax and context. The classification stage applies traditional models (SVM, Random Forest, Naïve Bayes, Logistic Regression) along with deep learning models (e.g., LSTM-based architectures), forming a hybrid system that improves accuracy and contextual understanding.

The framework follows four main stages: data collection from social media, text preprocessing, feature extraction, and hybrid classification. It is evaluated using standard metrics such as accuracy, precision, recall, and F1-score on benchmark datasets.

Experimental results show that the proposed hybrid NLP-LSTM model outperforms traditional machine learning methods, achieving the highest performance (about 95.7% accuracy), compared to SVM (91.4%), Random Forest (88.3%), and Naïve Bayes (84.6%). Overall, the study concludes that combining NLP with deep learning significantly improves sentiment prediction accuracy and robustness for real-world social media data.

Conclusion

The Hybrid NLP and Machine Learning Framework for Social Media Sentiment Prediction was evaluated using several sentiment classification techniques (based on NLP as well as machine learning) and with the use of benchmark social media data sets. The experimental results indicated that the incorporation of advanced NLP techniques in combination with machine learning and deep learning architectures significantly increased accuracy, context understanding, and overall predictive performance of the resulting systems. Among all the models evaluated, the Hybrid NLP-LSTM model demonstrated the highest overall performance with an accuracy of 95.7%, precision of 95.1%, recall of 94.8%, and F1-score of 94.9%. When compared to conventional machine learning techniques (Naïve Bayes, Random Forest, and Support Vector Machine), the Hybrid NLP-LSTM model had superior performance. The findings showed that the use of different types of contextual feature extraction techniques in combination with deep learning architectures results in a greater ability for Sentiment Prediction Systems to identify semantically related features and emotional patterns in complex and varied social media text data as well as via determining relationships between items in those types of data based on their context. Future work could include the integration of transformer-based large language models, multimodal sentiment analysis with text, images, and video data; XAI techniques, multilingual sentiment prediction, and real-time edge-based analytics to facilitate scalability and interpretability of next generation smart social media monitoring systems.

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Copyright

Copyright © 2026 Alok ., Sharad Kumar, Jagdeep Singh, Manoj Kumar, Sachin Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.