Using NLP for sentiment analysis

The approach presented in this section is based on the use case of classifying a high rate of incoming stream tweets. The task at hand is to extract the embedded sentiments within the tweets about a chosen topic. The sentiment classification quantifies the polarity in each tweet in real time and then aggregate the total sentiments from all tweets to capture the overall sentiments about the chosen topic. To face the challenges posed by the content and behavior of Twitter stream data and perform the real-time analytics efficiently, we use NLP by using a trained classifier. The trained classifier is then plugged into the Twitter stream to determine the polarity of each tweet (positive, negative, or neutral), followed by the aggregation and determination of the overall polarity of all tweets about a certain topic. Let's see how this...