I have included the complete notebook on my Github site, which can be found on my Github site.
Step 1 - Follow Ali's tutorial to establish an Apache Solr collection called "tweets"
Step 2 - Verify the version of Apache Spark being used, and visit the Solr-Spark connector site. The key is to match the version of Spark the version of the Solr-Spark connector. In the example below, the version of Spark is 2.2.0, and the connector version is 3.4.4
Step 9 - Filter the Tweets in the Spark DataFrame to ensure the timestamp and language aren't null. Once filter has been completed, add the sentiment value to the tweets.
val df_TweetSentiment = df.filter("text_t is not null and language_s = 'en' and timestamp_s is not null ").select($"timestamp_s", $"text_t", $"location", sentiment($"text_t").as('sentimentScore))