Description
Data Description: The four attached json files (savedtweets_americalatina.json, savedtweets_machinelearning.json, savedtweets_superleague.json, savedtweets_weibo.json), represent four separate classes of 100 tweets collected using a search query with the appropriate suffix. For example, saved_tweetsamericalatina.json has 100 tweets with the query “América Latina.”
Each tweet has up to seven characteristics (stored as key-value pairs): screen_name, text, location, lang, retweet_count, latitude*, and longitude*.
* Many tweets are missing these characteristics: see instructions below.
Instructions (Four parts in total):
Part 1. Load each json file into Python (obtaining a list of dictionaries for each) and perform the following:
a. discard any tweets that lack latitude (those without latitude will also lack longitude, and vice-versa)
b. Use the tweet-preprocessor to clean the text for each tweet using all available (default) options.
For each collection, save the modified list of tweets back into a new json file with the name prep_tweets_class#.json, where # matches the order of json files cited above (0=…americalatina, 1= …machinelearning, 2=..superleague, 3=weibo). You should have files savedtweets_class0.json, savedtweets_class1.json, savedtweets_class2.json, and savedtweets_class3.json at the end of the process.
Part 2. For each modified collection of tweets (i.e. after the transformation from part 1) calculate the # tweets with positive, negative, and neutral sentiment and depict these on a simple bar plot. You should have 3 bars per plot (one bar for positive, one bar for negative, one bar for neutral), and 4 plots total (one per tweet query class).
Part 3. Pool together all modified tweets into a single list, but maintain a combined secondary list of equal size that dictates the class (0, 1, 2, or 3) to which each tweet belongs. Ex: If there are 44 América Latina tweets at the beginning of the pooled list of tweets, the first 44 elements of the secondary list should be 0.
Part 4. Assume your combined lists each have a length of n. Your next goal is to construct a n x 5 numpy feature array suited for machine learning, where each row matches the corresponding index in your lists, and the 5 columns represent the features for the tweet at that position as follows:
Feature 1: The length of the tweet’s text.
Feature 2: The tweet’s retweet count.
Feature 3: The tweet’s latitude.
Feature 4: The tweet’s longitude.
Feature 5: one of two values as follows: 0 if the tweet is in English, or 100 otherwise.
For example, the first row in your feature array may look like the below:
[80. , 1. , 46.2380576 , 6.15323095, 100. ]
Part 5. Convert your secondary list of classes into an array, and then perform 10-fold cross-validation using three distinct classification estimators (either the ones we used in class, or those of your own choosing) to determine the accuracy available in using our features from part 4 in predicting the class of tweets.
Part 6. Using the t-SNE estimator to compress our features into 2 dimensions, visualize the tweets on a scatter-plot with 4 different colors for 4 different classes. Briefly comment (inline code comments are fine) on where you see distinct clusters of classes on the plot, and where you do not see any distinction.
