We can definitely add some copy around that. The scores that we're using are normalized on [0, 1] and broken into three categories - positive, negative, and neutral. It's possible for something to be all positive or all negative, but most things are a mix. The relative differences between positive and negative sentiment are the most interesting. For example, if you search Obama, you'll note that in September '13 the average positive and negative sentiment trends cross. On average, sentiment expressed in comments on stories mentioning Obama were more negative than positive. They later return to being more positive than negative, but this is the sort of thing that's interesting to investigate. In this particular case there were a string of very negative stories involving the NSA [1], the assassination of U.S. citizens [2] and Obamacare [3].

In the case of React you'll note an overall trend of increasingly positive sentiment and less negative sentiment. Sometimes it's fun to figure out what the spikes were by searching on HN. The big positive spike on 20015-07-15 was [4], a Show HN with very positive reception.

[1]: https://news.ycombinator.com/item?id=6347790

[2]: https://news.ycombinator.com/item?id=6435963

[3]: https://news.ycombinator.com/item?id=6462339

[4]: https://news.ycombinator.com/item?id=9891989

edit: formatting