Browsing by Subject "Sentiment analysis"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Comparative Analysis of Machine Learning based Classification Algorithms for Sentiment Analysis(Department of Computer Science & Information Technology, 2019) Yogi, Tekendra NathSentiment analysis is the process of predicting the sentiment polarity of review data based on a given data set. Nowadays, sentiment analysis is more popular in Internet in general and in social media in particular. In the web huge amount of review data generated in each day is rapidly increasing day to day so there need to process these data to detect the sentiment polarity of large review dataset as early as possible. In this research, comparison of three different machine learning based classification algorithms for sentiment analysis i.e. Multinomial naïve Bayes (MNB), K-Nearest-Neighbors (KNN) and Support Vector Machines (SVM) is presented. The main aim of this research is to evaluate their performance of those three different machine learning based classification algorithms for sentiment labeled sentences datasets with different size. The sentiment labeled sentences datasets used for this research is chosen such way that they are different in size, mainly in terms of number of instances. When comparing the performance of all three machine learning based classification algorithms for sentiment analysis, SVM is found to be better algorithm to detect sentiment polarity in all three sentiment labeled sentence datasets in every aspect, whereas MNB and KNN had got less performance in every aspect as compared to SVM. Keywords: Sentiment analysis, KNN, SVM, MNB.Item Stock Market Forecasting with LSTM and Sentiment Analysis(Department of Computer Science and Information Technology, 2020) Shrestha, AshishStock market is an industry where lots of data is generated daily and benefits are reaped on the basis of accurate prediction. Many people invest in stock market having some prediction and more luck. A decision in stock market plays an important role in the investor’s life. Also, stock market is a very complex system and non-linear in nature. So, then it is very difficult to analyse all the impacting factors before making a decision. Making decision with traditional techniques may be time consuming and may not ensure the reliability of the prediction. Data from stock market is a time series data and different variations of neural networks are widely being used for stock forecasting and prediction problems. Among various architectures of deep neural network, LSTM is one of the design that supports time steps of arbitrary sizes and is free of vanishing gradient problem. Furthermore, sentiment analysis is becoming popular in predicting the stock market behavior based on investors reactions. This research work studies the usage of LSTM networks on stock market prediction. Also, assuming that news articles have impact on stock market, this is an attempt to study the relationship between news and stock trend. From the result of the experiment carried out during this research work showed that the financial news do affect the stock market. Sentiment scores from financial news in addition to the stock indices do make the prediction or forecasting process more predictable. Keywords: Deep neural network, Deep learning, LSTM, Neural Network ,Stock Market, Sentiment Analysis, Time Series.