Twitter Sentiment Analysis Based On Adaptive Deep Recurrent Neural Network

Abstract

Deep learning classification has recently shown its Twitter logs promises in recent times. Many of the works are emotional on Twitter analysis is complete, but have not done the emotional impact survey sites on Twitter. In social media such as Twitter, Sentiment analysis has become a very important and difficult task. This environment requires a non-traditional method, such as data/tweet length, misspellings, abbreviations and special characters and properties of sentiment analysis tasks. Also, social media sentiment analysis is one of many interesting applications of basic questions. To overcome the issues in this work proposed, the method Adaptive Deep Recurrent Neural Network (ADRNN) using for this analysis is useful in evaluating microblogging Twitter data analysis of information. In the case of social network data, deep learning analyzes large amounts of data to achieve the effect of the traditional top machine learning algorithm. Concatenated text and location with Twitter sentiment analysis, especially Adaptive Deep Recurrent Neural Network (ADRNN) feature vectors work, deep learning classification methods can be used.To make full use of these data, developing a real-time Twitter sentiment analysis and visualization system. This is a Web application. Its purpose is a programming application package using the Python language to obtain real-time data from Twitter tags and keywords to use mining methods for Application Programming Interface (API) for tweets analysis. Twitter data is generated by applying different weighting schemes to improve the accuracy and F1 Score of the estimated classification.