Case Study: Tweet Sentiment Analysis with Geocoding and Mapping Following installing necessary modules, you will be able to Access the necessary information via using the Twitter API as can be found in the textbook of this course: Intro to Python for Computer Science and Data Science Learning to Program with AI, Big Data and The Cloud, Global Edition by Deitel \& Deitel Your program should ask the city or country or region and time interval to the user for specifying the place and time for acquiring the tweets. Once the tweets are obtained through the Twitter API, you analyze the data as of tweets. This analysis must include and/or enable the user to get the information for the following attributes: I) Trending topic (20pts) II) Word cloud (20pts) III) Sentiment analysis for a particular account or for the whole users from that region (20pts) IV) Visualize the tweets/trending topics on the map and show the words on a heat map (20pts) V) Determine the age and gender of an individual user (20pts)