The Power Of Opinions: Exploring Sentiment Analysis Techniques & Trends
DOI:
https://doi.org/10.26438/ijcse/v13i2.112123Keywords:
Sentimental Analysis, Machine Learning, Deep Learning, Healthcare, E-Commerce, social medi, Finance, NLP, BERT, Hybrid ModelsAbstract
Sentiment Analysis (SA) is a method used to analyze sentiment and opinions within textual data. It is extensively utilized across multiple industries, including business, healthcare, education, finance, and social media. This study examines various approaches to sentiment analysis, such as machine learning-based, lexicon-based, and hybrid techniques. Machine learning models, such as Support Vector Machines (SVM) and Naïve Bayes, are widely adopted but face challenges in understanding deeper contextual meanings. On the other hand, deep learning techniques like Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) improve accuracy by recognizing intricate text patterns. Hybrid approaches, which integrate machine learning with lexicon-based methods, enhance both interpretability and adaptability. This study also highlights emerging trends in sentiment analysis, such as emotion-based classification, aspect-based sentiment analysis, and the implementation of transformer-based models like BERT. Despite these advancements, challenges like sarcasm detection, real-time sentiment processing, and multilingual sentiment analysis persist. Addressing these challenges with advanced AI models, transfer learning, and domain-specific sentiment lexicons is essential for future improvements. As sentiment analysis continues to evolve, integrating deep learning, hybrid techniques, and transformer-based models will lead to better contextual understanding. Overcoming existing limitations will make the way for more accurate and profound sentiment analysis applications.
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