Intelligent Techniques for Emotion Detection in Humans and Emotional States in Plants for Creating a Healthy Environment

Emotion detection from text has gained significant attention in recent years due to its potential applications in various domains such as social media analysis, customer feedback analysis, and sentiment analysis. This research focuses on employing Natural Language Processing (NLP) techniques, including tokenizers and TF-IDF, along with different classifiers such as a hybrid model, LSTM model, and RF (Random Forest) model, for accurate emotion detection. The initial step involves data preprocessing, where tokenizers are utilized to break down the text into individual tokens or words, enabling further analysis. TF-IDF is then applied to assign weights to the tokens based on their frequency and importance in the document and across the corpus, respectively. This step helps identify the most significant words in the text data, allowing for a more focused analysis of emotions. Next, three different classifiers, namely a hybrid model, LSTM model, and RF model, are employed for emotion detection. The hybrid model combines the strengths of multiple ensemble models, including RF classifier, AdaBoost classifier, and Gradient Boosting classifier, using a voting classifier algorithm. The experimental findings provide strong evidence of the high accuracy achieved by both the hybrid model and LSTM model in detecting various emotions, such as happiness, sadness, fear, and anger. The hybrid model is also used to analyse psychological states in plants for creating a healthy environment. The hybrid model demonstrated exceptional performance, achieving an impressive testing accuracy rate of 94%, accompanied by precision and recall scores of 0.94 and 0.93, respectively. These results highlight the superior capability of these models in accurately classifying emotions from textual data. The robust performance of the hybrid model and LSTM model in emotion detection opens up numerous possibilities for their application in various fields. The ability to understand human emotions from text data can greatly inform decision-making processes in areas such as customer sentiment analysis, market research, social media monitoring, and psychological studies.