Social Media Sentiment Analyzer using NLP

Abstract

This research utilizes logistic regression as well as LSTM, SVM, and Random Forest to derive useful information from social media information found on websites such as Instagram, Facebook, and Twitter. Utilizing machine learning algorithms, we explore sentiment analysis, trend detection, user profiling, and content classification. Logistic regression is used for analyzing sentiment, an important element in grasping public opinion and brand perception. By applying natural language processing (NLP) methods, we classify social media posts based on whether they convey positive, negative, or neutral feelings. This enables businesses to track shifts in customer happiness and opinions as time progresses, supporting smart decision-making and proactive interaction with users. By adding logistic regression to our approach, we improve the depth of our investigation, giving companies more resources to uncover valuable information from social media data. This comprehensive strategy allows businesses to adjust to customer preferences, strategize effectively, and stay competitive in the digital realm of Facebook, Instagram, and Twitter.

Introduction

Social media has become a powerful platform for public expression, generating massive volumes of unstructured text. This presents both opportunities and challenges for analyzing public sentiment. Sentiment analysis, a subfield of Natural Language Processing (NLP), helps classify opinions in text as positive, negative, or neutral, supporting decision-making in business, politics, and research.

2. Challenges

• Social media content is often short, informal, and noisy.

• Manual analysis is impractical due to high data volume.

• Accurate sentiment extraction requires automated, scalable systems.

3. Machine Learning vs Deep Learning Approaches

• Traditional ML models like SVM, Logistic Regression, and Random Forest work well with structured features like TF-IDF and n-grams.

• Deep learning models, especially LSTM (Long Short-Term Memory) networks, handle the sequential nature of text better and capture context more effectively using word embeddings (e.g., Word2Vec, GloVe).

4. Literature Review Insights

• SVM performs well on structured text but struggles with noisy inputs.

• Ensemble methods (e.g., Random Forest, Gradient Boosting) improve robustness.

• LSTM models significantly outperform traditional ML methods in sentiment analysis, especially with pre-trained embeddings.

• Prior studies (e.g., Tang et al., Pang et al.) confirm deep learning’s superior performance on social media data.

5. Methodology

• Data preprocessing: Remove stopwords, emojis, URLs, and punctuation; tokenize and normalize text.

• Feature extraction: Use TF-IDF, n-grams, and word embeddings.

• Model training: Apply Logistic Regression, SVM, Random Forest (on TF-IDF), and LSTM (on embeddings).

• Evaluation metrics: Accuracy, Precision, Recall, and F1-Score.

6. Experimental Results

• The system classifies posts as Positive, Negative, or Neutral.

• Model performance (accuracy):

  • LSTM: 88% – highest accuracy, best at understanding context.

  • SVM: 85% – top among classical models.

  • Random Forest: 82%

  • Logistic Regression: 80%

• LSTM’s confusion matrix shows strong performance with minimal misclassifications.

Conclusion

This study presented a comparative analysis of sentiment analysis techniques applied to social media data using both traditional machine learning models and a deep learning approach. The methodology involved data preprocessing, feature engineering with TF-IDF and word embeddings, and the implementation of Logistic Regression, Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM) models. Experimental results demonstrated that while classical models such as SVM performed well on high-dimensional TF-IDF features, the LSTM model achieved superior accuracy by effectively capturing contextual and sequential patterns in text. The results confirm that deep learning methods are better suited for handling the complexities of natural language, particularly when dealing with unstructured and noisy data from social media platforms. The LSTM model’s performance, with an accuracy of around 88%, highlights its potential for real-world applications such as opinion mining, brand monitoring, and public sentiment tracking. However, traditional models remain valuable due to their simplicity, interpretability, and lower computational requirements, making them suitable for scenarios with limited resources. Future work can extend this study by incorporating transformer-based models like BERT or RoBERTa, which have shown state-of-the-art performance in NLP tasks. Additionally, expanding the dataset, including multimodal data (such as images and emojis), and applying domain-specific preprocessing techniques can further enhance accuracy. Overall, this research demonstrates that combining effective preprocessing, feature engineering, and deep learning techniques provides a robust framework for sentiment analysis in social media contexts.

References

[1] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” Proc. ACL-02 Conf. Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86, 2002. [2] T. Joachims, “Text categorization with Support Vector Machines: Learning with many relevant features,” Proc. 10th Eur. Conf. Mach. Learn. (ECML), pp. 137–142, 1998. [3] L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001. [4] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997. [5] D. Tang, B. Qin, and T. Liu, “Document modeling with gated recurrent neural networks for sentiment classification,” Proc. Conf. Empirical Methods in Natural Language Processing (EMNLP), pp. 1422–1432, 2015. [6] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint arXiv:1301.3781, 2013. [7] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” Proc. Conf. North American Chapter of the Association for Computational Linguistics (NAACL), pp. 4171– 4186, 2019. [8] B. Liu, Sentiment Analysis and Opinion Mining. San Rafael, CA: Morgan & Claypool, 2012.

Copyright

Copyright © 2025 Sachi Bagade, Bharti Zankar, Danish Sayyad, Rushikesh Shirsath, Prof. B. N. Babar, Prof. Ms. Bhagyashree Patil. 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.