Now it's your turn to run some topic models with social media text! 

The goal of this challenge is to understand the communication strategies of Donald Trump on Twitter. First, read the file that contains his tweets from the past year and a half. We will create a new variable called `month` to help us track changes in topic usage over time.

```{r}
library(quanteda)
library(streamR)
library(topicmodels)
tweets <- parseTweets("../data/trump-tweets.json")
tweets$date <- tweetscores::formatTwDate(tweets$created_at, "date")
tweets$month <- substr(tweets$date, 1, 7)
```

Create a corpus object and a DFM using options that seem appropriate to you.

```{r}

```

Run an LDA model. You may want to experiment with different number of topics or just stick to `K=30`.

```{r}

```

Look at the words most asociated with each topic for a sample of topics. Do you find that the results are valid according to the different definitions of validity we discussed earlier? Can you put labels to the topics?

```{r}


```

Pick a topic whose prevalence you think may have evolved over time and plot it. (For example, North Korea). What do you find?

```{r}


```

