A Time Series Analysis of Trends with Twitter Hashtags Using LSTM

Abstract

Social media has converged into our everyday life in such a way that from lifestyle to our behaviors, we follow the social media immensely. The influence of social media is easily observable whenever any `TREND' occurs in social media platforms and we stumble over the internet to follow that. Detecting trends can help to get notified about not only the ongoing topics around the internet as well as it gives us the chance to understand people's choices, emotions and so on. This study of trend analysis becomes more specific and invaluable when it is targeted for a specific genre or community. As such- Gaming, Movies and Tv series viewers, etc. There are very few assets in twitter for those specific genres which could assist its audiences or consumers to keep track of the trend list of that particular genre. Our chosen genre was Gaming. The primary purpose of our research is to analyze and predict the ongoing trends around Twitter of this specific community using Twitter Hashtags, which is a short yet quite stronger mode of expressing one's mood or the gist of any topic. Our contribution to the research reflects in applying the LSTM model of Recurrent Neural Network model in a time series dataset which is more convoluted and less explored area of research than the existing methods of analyzing regular time series models.