Hadoop-based Twitter Sentiment Analysis Using Deep Learning

Preview this chapter:

Hadoop-based Twitter Sentiment Analysis Using Deep Learning, Page 1 of 1

< Previous page | Next page > /docserver/preview/fulltext/9789815305395/chapter-28-1.gif

Sentiment analysis, the practice of classifying and identifying opinions displayed in audio, words, database reports, and tweets to ascertain if the opinion is positive, neutral, or negative, is of great interest to many individuals in the microblogging service arena. It could be challenging to extract sentiment from Twitter data due to its quirks. The research suggests a way to analyze sentiment using a Hadoop infrastructure with a deep learning classifier. In order to gather characteristics, data is distributed across Hadoop nodes. After that, the data from Twitter is parsed for the most crucial parts. The input data from Twitter is sorted into two categories, positive review along with negative review, via a deep learning algorithm, including a deep recurrent neural networks classifier. Some of the metrics used to measure performance include classification accuracy, specificity, and sensitivity. With a sensitivity of 0.9404 and a generality of 0.9157, the proposed technique surpassed traditional methods in classification with a precision of 0.9302.