Sentiment Knowledge Discovery in Twitter Using CoreNLP Library
Navjot Kaur, Arun Solanki · 2018
The growing popularity of social networking has brought about a rapid increase in the amount of data produced by users. Social networking web sites such as Twitter and Facebook provide a platform to millions of users to express their views about different services and products. Twitter is a great source of data and sentiment analysis can be used to refine this data into information. The proposed system performs sentiment analysis on Twitter data. The tweets data forms a dataset that cannot be handled by computing tools and techniques that have been traditionally used. Hadoop is the platform capable of handling such large datasets. Hence proposed system uses the Hadoop ecosystem for analyzing the sentiment of users. The classification is performed using a trained model from Stanford CoreNLP. With the help of Bigdata and Hadoop the proposed system analyzes the input text and classifies as per the provided labels. The existing systems using lexical techniques or machine learning algorithms have lower performance metrics. The proposed system overcomes these problems by using a combination of Hadoop for handling huge data and CoreNLP to augment the language processing capabilities of the system. The proposed system shows better results in comparison to existing systems.