Mood Detection and Emoji Classification using Tokenization and Convolutional Neural Network

Tokenization and convolutional neural networks (CNNs) is being used for the task of mood detection, emoji generation, and classification. Tokenization is helpful to break down text into individual sentences, words as well as phrases which is the important step in natural language processing (NLP). It allows the model to focus on individual words and phrases. The CNNs are then trained on labeled text datasets for mood detection, text-emoji pair datasets for emoji generation and emoji-label pair datasets for emoji classification. The results of the study indicate that tokenization and CNNs can be effectively used to understand the sentiment or emotion expressed in text data, to acquire high accuracy in classifying text as having a negative and positive or neutral sentiment, generating emojis that match the sentiment or emotion expressed in the text and classifying emojis as expressing a certain sentiment or emotion. For sentiment analysis, a CNN can be trained on a dataset of labeled text where the labels indicate the sentiment or emotion expressed. Once trained, the CNN can be used to classify new text as expressing a negative, neutral, or positive sentiment. As far as emoji generation is concerned, a CNN can be trained on a dataset that pairs text with corresponding emojis and labels. Once trained, the model can be used to generate emojis that align with the sentiment or emotion conveyed in new text inputs. As for emoji classification. This research study discusses about various techniques involved in tokenization emoji as well as text generation and classification.