Movie review summarization and sentiment analysis using rapidminer

Automatic text summarization is one of the important challenges of natural language tasks. It will help the readers save time to get the important information from a lengthy document automatically. Sentiment Analysis is the process of identifying and categorizing opinions expressed in a piece of text computationally to determine whether the writer's attitude towards a particular topic, product, etc., is positive, negative, or neutral. This research focuses on improving the summarization accuracy with sentiment analysis of movie review posts using RapidMiner operators. The first model of summarization is built using the Aylien Text Analysis extension. The proposed second model is built using the Text Processing extension. For both these methods the sentiment analysis is done using the same Aylien Text Analysis extension for evaluating the summarization results. An accuracy of 90% is achieved for sentiment analysis using the first model and 96% for the second model.