Implementing machine learning models for the stock's big data emerged as a component of algorithmic trading systems. This paper proposed a hybrid stock prediction model based on the collection of qualitative and quantitative data of particular stocks. In addition to tweets and news data, product reviews of the specific companies traded under National Stock Exchange are considered to analyze their effect on the stock movements. Historical Prices will be integrated with sentiment values generated from tweets, news and product reviews data to construct the amalgam model using Apache Spark and HDFS for storage of large data. The proposed model has been implemented in Google Cloud Platform with different cluster configurations. The paper compares the prediction accuracy based on various types of input data provided to the model using some popular machine learning algorithms.
Machine Learning Techniques and Big Data analytics are two central points of data science. Big Data is important for organizations to gain insights into it and machine learning techniques are one of the substantial assets for analyzing a massive amount of data. In this paper, a framework has been proposed to improve the short term stock trend prediction accuracy using Logistic Regression model by means of qualitative and quantitative data. This paper makes a comprehensive survey of stock market trend prediction with the accumulation of various data sources by applying machine learning techniques and by using big data analytics approach. The model has been implemented in Big data Framework with Hadoop and Apache Spark. For qualitative data Tweets sentiments and news sentiments has been taken in to account and for quantitative data Google trends and historical data are considered. The proposed system has enhanced the prediction accuracy about 3-4 % in comparison to existing models by supplying Google trend as input data in addition to market sentiments and historical data. The implemented model can help the investors to take short term decisions to make money in the security market and the survey would help in finding the most effective resources which overly influence the stock prices.
The whole human race is acquainted with the truth that COVID-19 has taken the form of a pandemic. Almost, all the countries are endeavouring their best to circumscribe the dispersion as much as possible. This paper focuses to observe sentiments of Indians during a nationwide lockdown to find what was going on in people's minds due to lockdown and its extension announced by the Indian government. Data has collected from Twitter during the second lockdown period. The results revealed that the majority of the people shows a positive attitude for declared lockdown and need the extension of the lockdown for a month or two to control the spread across the country. Keywords: COVID-19, Lockdown, Pandemic, Sentiments, Twitter
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