Abstract: Next word prediction using machine learning which includes Natural Language Processing (NLP) is a significant part of artificial Intelligence, which incorporates AI, which contributes to finding productive approaches to speak with people and gain from the associations with them. One such commitment is to give portable clients anticipated” next words,” as they type along within applications, with an end goal to assist message conveyance by having the client select a proposed word as opposed to composing it. As LSTM is Long short time memory it will understand the past text and predict the words which may be helpful for the user to frame sentences and this technique uses a letter-to-letter prediction means it predicts a character to create a word. As writing an essay and framing a big paragraph are time-consuming it will help end-users to frame important parts of the paragraph and help users to focus on the topic instead of wasting time on what to type next. We expect to create or mimic auto-complete features using LSTM. Most of the software uses different methods like NLP and normal neural networks to do this task we will be experimenting with this problem using LSTM by using the Default Nietzsche text file also known as our training data to train a model.
Keywords: Predection, Information
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DOI:
10.17148/IJARCCE.2022.11773
