• Madhav Sahu
• Santosh Nagar
• Anurag Shrivastava

In recent years, opinion mining has emerged as a critical field within natural language processing, driven by the exponential growth of user-generated content on social media and other online platforms. This paper presents an advanced opinion mining framework that leverages hybrid deep learning models to enhance the accuracy and efficiency of sentiment analysis. By integrating convolutional neural networks (CNNs) with long short-term memory (LSTM) networks, the proposed model effectively captures both local and global contextual information from textual data. Additionally, we incorporate traditional machine learning techniques to further refine the sentiment classification process. Extensive experiments conducted on benchmark datasets demonstrate that our hybrid approach significantly outperforms existing state-of-the-art methods in terms of precision, recall, and F1-score. The results underline the potential of hybrid deep learning models to address the complexities of opinion mining, offering robust solutions for real-world applications such as market analysis, customer feedback evaluation, and social media monitoring.

Sentiment Analysis, Opinion Mining, Hybrid model, CNN, LSTM, Deep Learning

"Advanced Opinion Mining with Hybrid Deep Learning Model ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.f756-f759, June-2024, Available :http://www.jetir.org/papers/JETIR2406586.pdf

"Advanced Opinion Mining with Hybrid Deep Learning Model ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppf756-f759, June-2024, Available at : http://www.jetir.org/papers/JETIR2406586.pdf