• Hriday G
• Surya Shekar B S
• Vishwas M
• Yogeshwar H N
• Mahadeva Prasad Y N

Sentiment analysis is a field of natural language processing that involves analysing text data to determine the sentiment or emotion conveyed by it. Deep learning is a powerful technique that has shown remarkable success in sentiment analysis tasks. Support Vector Machines (SVM) and K-means clustering are two popular techniques used in sentiment analysis. SVM is a machine learning algorithm that can be used for both classification and regression tasks. In the context of sentiment analysis, SVM can be used to classify text into positive or negative sentiment. The algorithm works by identifying a hyperplane that separates the data points into two classes. The hyperplane is chosen in such a way that it maximizes the margin between the two classes. K-means clustering is a clustering algorithm that partitions data points into k clusters. In the context of sentiment analysis, K-means can be used to group similar text data together. The algorithm works by iteratively assigning data points to the nearest cluster center, and then recalculating the cluster center based on the new assignments. In recent years, deep learning models such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have shown remarkable success in sentiment analysis tasks. These models can learn complex representations of text data and can capture the subtle nuances of language that are difficult to capture using traditional techniques. SVM and K-means clustering are two popular techniques used in sentiment analysis, and deep learning models such as CNN and RNN have shown remarkable success in recent years. These techniques and models can be used to analyze text data from various sources such as social media, customer reviews, and news articles, and can provide valuable insights into the sentiment and emotions of people towards a particular topic or product.

"Sentimental Analysis Based On Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.b676-b680, May-2023, Available :http://www.jetir.org/papers/JETIR2305188.pdf

"Sentimental Analysis Based On Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppb676-b680, May-2023, Available at : http://www.jetir.org/papers/JETIR2305188.pdf