Big data approach for sentiment analysis of twitter data using Hadoop framework and deep learning
Sentiment analysis acquired a great area of attention in the microblogging websites and analysis of sentiment is a practice of categorization and identification of opinions that are articulated as speech, text, database sources and tweets to detect if opinion is negative, positive or neutral. The challenge lies in determining sentiment from the tweets due to the unique characteristics of Twitter data. This paper presents an approach for sentiment analysis by adapting a Hadoop framework and deep learning classifier. The Hadoop cluster is used for the distribution of data for extracting the features. Then, the significant features are extracted using the twitter data. The deep learning classifier, namely deep recurrent neural network classifier is used assign a real-valued review to each input twitter data thus, classifying the input data into two classes, such as positive review and negative review. The analysis of the performance is done using metrics like, classification accuracy, sensitivity and specificity. In contrast to classical strategies, the proposed method offered improved classification accuracy of 0.9302, better sensitivity of 0.9404 and high specificity of 0.9157, respectively.
Sentiment analysis, feature extraction, feature classification, Hadoop cluster, deep recurrent neural network