Text Classification Using N-Grams for Providing Effective Response in Chatbot

Text classification using N-Grams can be a useful approach for providing effective responses in a chatbot. N-Grams are contiguous sequences of N words in a given text, and they can capture important patterns and contextual information. Text classification is a fundamental task in chatbot development, aiming to assign appropriate categories or labels to user input messages. One effective approach is the use of N-Grams, which are contiguous sequences of N words in a text. This paper presents a method for using various library for generating N-Grams in text classification to enhance the effectiveness of chatbot responses. Once the model is trained and evaluated, it is integrated into the chatbot. The user's input message is preprocessed, and N-Grams are extracted and encoded into numerical features. The trained classifier predicts the appropriate category or label for the user's query, enabling the chatbot to provide a pre-defined response or trigger a specific action based on the predicted label. The result shows that the utilization of N-Grams in text classification enhances the capabilities of chatbots, enabling them to provide more effective responses. By leveraging N-Grams as features and employing machine learning algorithms, chatbots can categorize user input accurately, leading to improved user experiences and interactions.