International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 2112 LSTM Model for Semantic clustering of user-generated content using AI Geared to wearable Device Dr. T.Suresh 1 , Dr. K.T. Meena Abarna 2 1,2 Assistant Professor, Dept. of Computer Science and Engineering, Annamalai University, Tamilnadu, India. ------------------------------------------------------------------***--------------------------------------------------------------- Abstract - In this paper we propose and investigate a novel end-to-end method for automatically generating short message responses, called Smart Reply. It generates semantically diverse suggestions that can be used as complete message responses with just one tap on wearable. The system is currently used in Inbox by Message and is responsible for assisting with 10% of all wearable responses. It is designed to work at very high throughput and process hundreds of millions of messages daily. The system exploits state-of-the-art, large-scale deep learning. We describe the architecture of the system as well as the challenges that we faced while building it, like response diversity and scalability. We also introduce a new method for semantic clustering of user-generated content that requires only a modest amount of explicitly labeled data. Key Words: Message, LSTM, Deep Learning, Clustering, Semantics. 1. INTRODUCTION Message is one of the most popular modes of communication on the Web. Despite the recent increase in usage of social networks, message continues to be the primary medium for billions of users across the world to connect and share information [2]. With the rapid increase in message overload, it has become increasingly challenging for users to process and respond to incoming messages. It can be especially time-consuming to type message replies on a wearable device. In Computer Science, the field of AI research defines itself as the study of Dzintelligent agentsdz: any device that perceives its environment and takes actions that maximize its chance of success at some goal. The term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. Wearable and the Internet of Things (IoT) may give the impression that it’s all about the sensors, hardware, communication middleware, network and data but the real value is in insights. In this article, we explore artificial intelligence (AI) and machine learning that are becoming indispensable tools for insights. Artificial Intelligence: The field of artificial intelligence is the study and design of intelligent agents able to perform tasks that require human intelligence, such as visual perception, speech recognition, and decision- making. In order to pass the Turing test, intelligence must be able to reason, represent knowledge, plan, learn, communicate in natural language and integrate all these skills towards a common goal. Machine Learning: The machine learning is the subfield that learns and adapts automatically through experience. It focuses on prediction, based on properties learned from the training data. The origin of machine learning can be traced back to the development of neural network model and later to the decision tree method. Supervised and unsupervised learning algorithms are used to predict the outcome based on the data. Fig -1 The Components of Smart Reply No Smart Reply Trigger No response? Permitted responses and clusters Response Selection (LSTM) Yes No Preprocess Message Diversity Selection Smart Reply Suggested