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
& 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 was unconventional and
more complex than the existing methods of analyzing regular time series models.