Application of Machine Learning to Developing an Internet Community Model

This study will assist in determining the types of elements and occasions that influence people's opinions. It aims to implement a social media sentiment Social Media Sentiment Analysis using machine learning techniques. The objectives of this study are as follows: (i) To combine natural language processing, machine learning, and financial modeling to analyze the different impacts of sentiments on social media. (ii) To implement the developed model in (i) (iii) To evaluate the performance of the developed model in (ii). The method used in this study includes the application downloads of tweets from Tweeter and inserts the data into the MongoDB database. The Natural Language Toolkit corpus called Twitter samples is available for the training dataset, the application extracts features from the training dataset. The insights would help the people for example in the National Security Department and Emergency Response Teams to understand the public emotional behaviors towards certain events and people, take appropriate actions with informed decisions, and perform situational analysis regarding public safety and security. Pymongo retrieved the text driver from the Tweets. The classifier model assigned polarity to each tweet and displayed the Tweet. Data visualization was successful for the retrieved system for Twitter user followers, friends, status counts, and charts to visualize the data. The application displayed exploratory visualization using line charts, bar charts, and tweets on a map using coordinates.