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Author: Behera, Ranjan KumarCOAR Access Rights: metadata only access

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    Article
    Citation - WoS: 197
    Citation - Scopus: 296
    Co-Lstm: Convolutional Lstm Model for Sentiment Analysis in Social Big Data
    (Elsevier Sci Ltd, 2021) Behera, Ranjan Kumar; Jena, Monalisa; Rath, Santanu Kumar; Misra, Sanjay
    Analysis of consumer reviews posted on social media is found to be essential for several business applications. Consumer reviews posted in social media are increasing at an exponential rate both in terms of number and relevance, which leads to big data. In this paper, a hybrid approach of two deep learning architectures namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (RNN with memory) is suggested for sentiment classification of reviews posted at diverse domains. Deep convolutional networks have been highly effective in local feature selection, while recurrent networks (LSTM) often yield good results in the sequential analysis of a long text. The proposed Co-LSTM model is mainly aimed at two objectives in sentiment analysis. First, it is highly adaptable in examining big social data, keeping scalability in mind, and secondly, unlike the conventional machine learning approaches, it is free from any particular domain. The experiment has been carried out on four review datasets from diverse domains to train the model which can handle all kinds of dependencies that usually arises in a post. The experimental results show that the proposed ensemble model outperforms other machine learning approaches in terms of accuracy and other parameters.
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    Article
    Citation - WoS: 17
    Citation - Scopus: 30
    Comparative Study of Real Time Machine Learning Models for Stock Prediction Through Streaming Data
    (Graz Univ Technolgoy, inst information Systems Computer Media-iicm, 2020) Behera, Ranjan Kumar; Das, Sushree; Rath, Santanu Kumar; Misra, Sanjay; Damasevicius, Robertas; Computer Engineering
    Stock prediction is one of the emerging applications in the field of data science which help the companies to make better decision strategy. Machine learning models play a vital role in the field of prediction. In this paper, we have proposed various machine learning models which predicts the stock price from the real-time streaming data. Streaming data has been a potential source for real-time prediction which deals with continuous flow of data having information from various sources like social networking websites, server logs, mobile phone applications, trading floors etc. We have adopted the distributed platform, Spark to analyze the streaming data collected from two different sources as represented in two case studies in this paper. The first case study is based on stock prediction from the historical data collected from Google finance websites through NodeJs and the second one is based on the sentiment analysis of Twitter collected through Twitter API available in Stanford NLP package. Several researches have been made in developing models for stock prediction based on static data. In this work, an effort has been made to develop scalable, fault tolerant models for stock prediction from the real-time streaming data. The Proposed model is based on a distributed architecture known as Lambda architecture. The extensive comparison is made between actual and predicted output for different machine learning models. Support vector regression is found to have better accuracy as compared to other models. The historical data is considered as a ground truth data for validation.