## Authenticating Twitter
The chunk of code below is used to authenticate your Twitter developer account. You'll need to navigate here: https://developer.twitter.com/en.html and follow the instructions to get a developer account, setup an app for access tokens, and generate tokens and secret keys to sign in. I've included the code here (minus my secret access keys!) to show you how it might be done.

```{r}
library(twitteR)

consumer_key <- ""
consumer_secret <- ""
access_token <- ""
access_secret <- ""

setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)
```

## Search Twitter
Replace the #REPLACEME! hashtag below with a hashtag of your own choosing. Search for 1000 Tweets containing that hashtag and convert them to a dataframe. (HINT: It might be useful to go on Twitter and see what hashtags are trending currently.)

Please make sure you're searching with `lang = 'en'` as the tools we've used in class focus on analyzing English-language documents.

```{r}
ls_twitter <- searchTwitter("#REPLACEME! -filter:retweets", n = 1000, lang = "en")
df_twitter <- twListToDF(ls_twitter)
```

## Word count
1. Tokenize your Twitter corpus and generate a word count.
2. Using the `TidyText` package, remove stop words and generate a new word count.
3. Create a visualization of the word count distribution and interpret your results.

```{r}

```

## Tf-idf
1. Generate a tf-idf measure of words in your dataset.
2. Create a visualization of the tf-idf measure and interpret your results.
3. Is the basic word count or the tf-idf more appropriate for your analysis?

```{r}

```

## Sentiment analysis
1. Using the built-in sentiments from `TidyText`, generate sentiment counts for your words using either the basic word count or tf-idf measure from above.
2. Create a visualization of the sentiment measure. Interpret your results.

```{r}

```

## Interpretation
Write a paragraph interpreting the word count, tf-idf, and sentiment of the words in your dataset. What do these tell us about the conversations surrounding your hashtag (if anything)? How could you improve this analysis?
