The intent of this chapter was to illustrate how various data science tasks can be integrated into an application. We chose an application that processes tweets because it is a popular social medium and allows us to apply many of the techniques discussed in earlier chapters.

A simple console-based interface was used to avoid cluttering the discussion with specific but possibly irrelevant GUI details. The application prompted the user for a Twitter topic, a sub-topic, and the number of tweets to process. The analysis consisted of determining the sentiments of the tweets, with simple statistics regarding the positive or negative nature of the tweets.

The first step in the process was to build a sentiment model. We used LingPipe classes to build a model and perform the analysis. A Java 8 stream was used and supported a fluent style of programming where the individual processing steps could be easily added and removed.

Once the stream was created, the JSON raw text was processed and used to initialize a TweetHandler class. Instances of this class were subsequently modified, including converting the text to lowercase, removing non-English tweets, removing stop words, and selecting only those tweets that contain the sub-topic. Sentiment analysis was then performed, followed by the computation of the statistics.

Data science is a broad topic that utilizes a wide range of statistical and computer science topics. In this book, we provided a brief introduction to many of these topics and how they are supported by Java.