Dictionary methods are often sensitive to the presence of specific words that may appear very frequently in a document. Here you will explore whether that could be an issue in the example we just saw. The goal of this challenge is to replicate the sentiment analysis example but excluding the word "great". 

The first step is to exclude this word from the lexicon:

```{r}
# YOUR CODE HERE

# remove word "great" from dictionary
lexicon <- lexicon[-which(lexicon$word=="great"),]


```

Now, run the sentiment analysis again and examine the results. Did anything change?

```{r}

```

We will now examine whether this result still holds when we look at a different source of text -- the campaign speeches you scraped earlier this semester. Load that dataset (also available as `../data/campaign-speeches.csv`) into R. Then, subset the dataset so that it only includes speeches from 2016, to make sure we can compare what we have for each candidate.

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

```

Create two corpus objects, one for each candidate. Use the `kwic` function to examine in what context the word `great` appears in each case

```{r}


```

Compute the average and median length (in characters AND in number of words) for the speeches by each candidate. Who gave the longest speeches?

```{r}


```


Now, produce two word clouds based on each corpus object. What do you learn from this analysis?

```{r}


```

Finally, replicate the sentiment analysis we conducted earlier, but this time using campaign speeches. Which of the two candidates is more positive? Do you get the same result as when you used tweets?

```{r}

```



