Sentiment Analysis Using Natural Language Processing

Abstract

In this research, Our initial efforts on text based sentiment detection of tweets are presented in this publication. The motive of this experiment is to extract sentiment from tweets by using subjects that are present in them. Using methods from Natural Language Processing(NLP), it determines the sentiment that relevant to the specific topic. The subjective aspect classification, semantic connection, and polarity categorization are the three primary processes in our experiment that are used to classify sentiment. By establishing experiment uses sentiment lexical terms to determine the grammatical relationship between subject matter and sentiment lexicons. The suggested approach outperforms the existing text sentiment analysis technologies, according to experimental data, because tweets\' structure differs from that of conventional text. The categorization of emotions in textual data is known as sentiment analysis, sometimes called opinion mining or emotion extraction. This approach has been widely used over time to pinpoint the feelings and ideas present in a particular textual information set. People mostly use Twitter as a means of social media to communicate their feelings on specific events. In this study, we gathered tweets for different occurrences, evaluated them using several automated learning methods, including LSTM, the Random Forest classifier, SVM, and Naïve Bays, and compared the outcomes.

Introduction

1. Background and Importance of Sentiment Analysis

• Before the internet, businesses relied on sales data and referrals to understand customer preferences.

• The rise of social media allowed users to share emotions and opinions, leading to the development of Sentiment Analysis to extract meaning from this data.

• SA is used in many sectors like customer service, healthcare, finance, and politics, enabling better understanding of public sentiment and behavior.

2. Definition and Goals of Sentiment Analysis

• SA analyzes text to determine emotional tone—positive, negative, or neutral.

• It goes beyond classification to detect underlying emotional intent, including specific emotions such as happiness, sadness, or anger.

• This is especially helpful when processing large volumes of customer feedback, like in restaurant reviews or e-commerce.

3. Levels of Sentiment Analysis

• Document Level: Analyzes the overall sentiment of the entire text.

• Sentence Level: Evaluates sentiment sentence by sentence for more granular insight.

• Aspect/Feature Level: Targets specific features (e.g., product quality, service) within a review.

4. Evolution of Sentiment Analysis

• SA emerged around 2000 with growing internet content.

• It evolved from rule-based and lexicon-based methods (using predefined word dictionaries) to machine learning (ML) and deep learning (DL) approaches.

• Advanced models like ChatGPT and CNN-LSTM architectures now integrate sentiment detection with real-time decision-making.

5. Methodologies

• Lexicon-Based: Uses dictionaries to assign emotional scores to words.

• Machine Learning-Based: Employs algorithms like SVM, Naïve Bayes, and Decision Trees using features like TF-IDF and n-grams.

• Deep Learning-Based: Uses models like LSTM and BiLSTM for sequence analysis and better context awareness.

• Hybrid Approaches: Combine lexicon, ML, and DL methods for improved accuracy and robustness.

6. Emotion Detection from Handwritten Text

• Handwritten inputs are converted to text using OCR.

• Challenges include variability in handwriting, cursive text, and document noise.

• Preprocessing techniques like noise reduction, high-res input, and layout optimization help improve OCR performance.

7. Tools and Techniques

• Natural Language Toolkit (NLTK): Used for preprocessing steps like stopword removal and lemmatization.

• Word Embeddings: Models like Word2Vec and GloVe convert words into vectors that capture semantic relationships.

• Optimization: Models often use Adam optimizer, logistic loss functions, and batch training to fine-tune performance.

8. Research Objectives

• Develop a unified model for sentiment and emotion detection from both electronic and handwritten text.

• Evaluate various ML/DL techniques for performance and accuracy.

• Create a comprehensive dataset to improve model training.

• Derive actionable insights from textual sentiment data.

9. Literature Review Highlights

• Studies have explored text-based, multimodal, and Twitter-based sentiment analysis.

• Researchers addressed issues like differentiating between emotion-related terms and validating emotion labels using hashtags.

• Advanced applications include POI recommendation systems and emotion detection via typing patterns.

Conclusion

Sentiment Analysis is a dynamic and evolving field that leverages NLP, ML, and DL to decode emotions in both digital and handwritten texts. Its applications span across industries and play a vital role in data-driven decision-making, brand analysis, mental health monitoring, and user engagement.

References

[1] Sentiment Analysis: Capturing Favorability Using Natural Language Processing\" • Published in: Proceedings of the Second International Conference on Web Search and Data Mining, 2023.\"Sentiment Analysis and Subjectivity\" • Authors: Liu, B., & Zhang, L.Published in: Handbook of Natural Language Processing, Second Edition, 2010. • Link: Wikipedia \"Mining and Summarizing Customer Reviews\" • Authors: Hu, M., & Liu, B. 2013 • Link: Wikipedia • 365618365_Sentiment_Analysis_of_Twitter_Data [2] Text Analysis Using Deep Neural Networks in Digital Humanities and Information Science • Authors: Omri Suissa, Avshalom Elmalech, Maayan Zhitomirsky-Geffet • Published: July 30, 2023 • Summary: This paper explores the application of deep neural networks in digital humanities and information science, highlighting their effectiveness in tasks such as spell checking, language detection, entity extraction, and author detection. It also discusses challenges like data availability and domain adaptation. • Link: arXiv:2307.16217arXiv [3] Text Mining with Network Analysis of Online Reviews and Consumers’ Satisfaction: A Case Study in Busan Wine Bars • Authors: Wei Fu, Eun Kyong Choi, Hak Seon Kim • Published: March 1, 2022 • Summary: This study employs text mining and network analysis to examine online reviews of wine bars in Busan, South Korea. It identifies key attributes influencing customer satisfaction and visualizes relationships among review topics. • Link: E-Grove Repositoryegrove.olemiss.edu+1ResearchGate+1 [4] Augmenting Text Mining Approaches with Social Network Analysis to Understand the Complex Relationships among Users\' Requests: A Case Study of the Android Operating System • Authors: Chan Won Lee, Sherlock A. Licorish, Bastin Tony Roy Savarimuthu, Stephen G. MacDonell • Published: March 26, 2021 • Summary: This paper integrates text mining with social network analysis to understand user feedback on Android applications. It reveals feature-related issues and their interdependencies, providing insights for software improvement. • Link: arXiv:2103.14761arXiv [5] Text Mining Using Natural Language Processing • Authors: Drashti Panchal, Mihika Mehta, Aryaman Mishra, Saish Ghole, Mrs. Smita Dandge • Published: 2022

Copyright

Copyright © 2025 Anshika Tripathi. 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.