In this challenge you will practice text classification at scale. The file `econnews.csv` contains 1,000 articles about the economy published in different newspapers that were labeled by human coders as either positive (1) or negative (0) in the `Sentiment` variable. The goal is to try to replicate human coding using a supervised learning algorithm.

First, read the dataset in R and create a DFM using the quanteda package. Think carefully how you want to preprocess this dataset -- for example, do you want to keep punctuation? numbers? stopwords? n-grams?

```{r}
d <- read.csv("../data/econnews.csv", stringsAsFactors=F)


```

Now, choose one of the classifiers we just saw and run it with this new dataset. Make sure you first run it only with a random subset (the training dataset).


```{r}


```


Compute its performance (accuracy, precision, recall) on the test set. How well did it perform? Why?

```{r}


```

