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[ reached getOption("max.print") -- omitted 2 rows ]

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The thing to notice here is that there are several different Greenvilles, so this makes analysis of the local area pretty hard. Many of the tweets can be about Greenville, NC or SC. In this particular dataset, there was even a Greenville Road in California (where there was a car fire). Rather than play a filtering game, it may be better to apply some knowledge specific to the area. For instance, local tweets will often be tagged with `#yeahThatgreenville`. So we will search again for the `#yeahthatgreenville` hashtag (and add a few more tweets as well). This time, we'll keep retweets:

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I used the `select` function from `dplyr` to keep only the `id` and `text` fields. The `unnest_tokens()` functions creates a long dataset with a single word replacing the text. All the other fields remain unchanged. We can now easily create a bar chart of the words used the most:

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word lexicon

<chr> <chr>

1 a SMART

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The `anti_join` function is probably not familiar to most data scientists or statisticians. It is the opposite of a merge in a sense. Basically, the command above merges the `tweet_words` and `my_stop_words` data frames, and then _removes_ the rows that came from the `my_stop_words` dataset, leaving only the rows in `tweet_words` (the `id` and `word`) that does not match with something from `my_stop_words`. This is desirable because our `my_stop_words` dataset contains words we _do not_ want to analyze.

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word sentiment

<chr> <chr>

1 2-faced negative

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Once you get to this point, sentiment analysis can start fairly easily:

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sentiment n

<chr> <int>

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There are many more positive words than negative words, so the mood tilts positive in our crude analysis. We can also group by tweet, and see whether there more more positive or negative tweets:

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sentiment n

<chr> <dbl>

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There is, of course, a lot more that can be done, but this will get you started. For some more sophisticated ideas you can check [Julia Silge's analysis of Reddit data](http://juliasilge.com/blog/Reddit-Responds/), for instance. Another kind of analysis looking at sentiment and emotional content can be found [here](https://mran.microsoft.com/posts/twitter.html) (with the caveat that it uses the predecessor to `dplyr` and thus runs somewhat less efficiently). Finally, it would probably be useful to supplement the above sentiment data frames with situation-specific sentiment analysis, such as making `goallllllll` in the above a positive word.