Integrating NLP with Random Forest for Emotion Detection
Mansi Bhati, Kanika Sharma, Dhyanendra Jain, Anjani Gupta, Shruti Agarwal · 2024
NLP enables computers to understand and process human language. It has become an integral technology for machine translation, spam detection, and medical applications. This section could outline key NLP methods like syntax analysis, semantic analysis, and statistical NLP. It could also describe evolutionary and revolutionary changes in NLP techniques over time. The performance of the proposed method - Random Forest, is estimated by utilizing the Emotion data consisting of various human emotions expressed as comments. The proposed method has acquired an accuracy of 95.08% as compared to the other existing techniques. Recent advances like neural networks and deep learning enable more nuanced NLP applications. This section could highlight new techniques and future directions like conversational AI and multilingual NLP. Despite progress, NLP still faces challenges in understanding context, sarcasm, dialects, etc. This section could summarize existing weaknesses and difficulties like bias in training data. In conclusion, NLP has become a versatile technology, evolving from rule-based to statistical to neural techniques. Applications will continue to expand as methods improve in accuracy and flexibility. However, challenges remain in reaching human-level language understanding.