• Prof Vijay Khandar
• Shubham Kaware
• Tanvi Kharate
• Yash Agrawal
• Sanskruti Dhomane

This project presents an innovative approach to emotion identification through sentiment analysis, utilizing machine learning algorithms such as Support Vector Machines (SVM), Logistic Regression, and Random Forest. Emotion identification from text plays a crucial role in various applications ranging from social media analysis to customer feedback processing and mental health assessment. Traditional sentiment analysis techniques often fall short in capturing the nuanced emotional nuances conveyed in human communication. we develop a visualization component to represent the output of emotion identification intuitively. Emojis are employed to convey the detected emotions, providing a visually appealing and universally understandable representation of emotional states. Additionally, we generate graphs to depict the distribution of emotions across the analyzed textual data, enabling users to gain insights into prevalent emotional patterns and trends. To evaluate the effectiveness of our methodology, we conduct experiments on diverse datasets from social media platforms, online forums, and customer reviews. The results demonstrate that our approach outperforms existing sentiment analysis methods in accurately identifying a range of emotions including happiness, sadness, anger, surprise, fear, and disgust.

- Emotion identification ,Sentiment analysis, Machine learning, Support Vector Machines (SVM), Logistic Regression, Random Forest, Emoji

"Emotion Identification Using Sentimental Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.f64-f68, April-2024, Available :http://www.jetir.org/papers/JETIR2404508.pdf

"Emotion Identification Using Sentimental Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppf64-f68, April-2024, Available at : http://www.jetir.org/papers/JETIR2404508.pdf