I embarked on a journey to develop a chatbot capable of recognizing user intents and providing meaningful responses. Here's an overview of my project:
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I started by collecting a dataset of user intents and corresponding responses. The dataset contained various intent categories, including greetings, queries, jokes.
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To process and understand user input, I utilized Natural Language Processing techniques. This included tokenization, lemmatization, and text cleaning to prepare the data for further analysis.
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Neural Network Approach:
- I implemented a neural network model using TensorFlow and Keras to classify user input into predefined intent categories. This involved creating a bag-of-words representation of the text data and training the model to predict the intent based on user queries.
- Cosine Similarity Approach:
- In addition to the neural network, I experimented with a cosine similarity-based approach. This method involved comparing user input to a predefined dataset of intents to find the most similar response.
This project has been an exciting exploration into the world of chatbot development, NLP, and machine learning. It highlights the potential for chatbots to enhance user experiences and streamline interactions in various domains.