Analysis of stock market using text mining and natural language processing

Abdullah, Sheikh S.; Rahaman, Mohammad Saiedur; Rahman, Mohammad Saidur

doi:10.1109/iciev.2013.6572673

Cited by 15 publications

(6 citation statements)

References 2 publications

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“…Sentiment analysis has been successfully applied to predict if a certain stock or index is "bullish" (buy signal) or "bearish" (sell signal) to some extent. In this paper "Analysis of Stock Market using Text Mining and Natural Language Processing" [6] Sheikh Shaugat Abdullah et al, propose a new data processing framework that takes text from different sources as input where the source may be authentic or unauthentic in the context of Bangladesh markets. Official information like changes in outstanding shares, bonus or dividend, mergers or acquisitions etc.…”

Section: B Fundamental Analysis Using Natural Language Processing Tementioning

confidence: 99%

Predicting Indian Stock Prices through Sentiment Score and Data Mining Techniques

Govind*,

Prasanth N,

kumar

et al. 2019

IJRTE

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Much is being said about the use of artificial intelligence to assist in the financial markets but there is a surprising lack of actual data supporting one trading algorithm over another. While there are numerous research that explore the possibilities of machine learning and deep learning techniques for stock market prediction, due to the lack of inter-domain expertise (people who are experts in both data science and finance), most of these projects use relatively elementary methods and fail to account for many underlying constraints. This paper aims to create a stock market investment aide which predicts the price action of stock market instruments using neural networks that learn time series patterns in the historical price data, correlates the prediction with the sentiment score of the market for that day and thereby give the investor/trader a buy or sell signal. The model developed has been successful in predicting price action of the NIFTY50 index with an accuracy of 89 percent. The prediction aided with the correlation from the sentiment analysis can give the investor an added confidence while making investment decisions on the stock market.

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Section: B Fundamental Analysis Using Natural Language Processing Tementioning

confidence: 99%

Predicting Indian Stock Prices through Sentiment Score and Data Mining Techniques

Govind*,

Prasanth N,

kumar

et al. 2019

IJRTE

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“…This study achieved an accuracy of prediction ranging from 60 % to 65%. S.Abdullah et al [20] analyzed Bangladesh stock market using text mining and NLP techniques to extract fundamental information from textual data. This study used the information parser algorithm and Apache OpenNLP which is a java based machine learning toolkit for natural language processing to analyze textual data related to the stock market.…”

Section: B Studies Relaying On News Analysismentioning

confidence: 99%

Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis

Khedr¹,

Salama²,

Yaseen³

2017

IJISA

116

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Stock market prediction has become an attractive investigation topic due to its important role in economy and beneficial offers. There is an imminent need to uncover the stock market future behavior in order to avoid investment risks. The large amount of data generated by the stock market is considered a treasure of knowledge for investors. This study aims at constructing an effective model to predict stock market future trends with small error ratio and improve the accuracy of prediction. This prediction model is based on sentiment analysis of financial news and historical stock market prices. This model provides better accuracy results than all previous studies by considering multiple types of news related to market and company with historical stock prices. A dataset containing stock prices from three companies is used. The first step is to analyze news sentiment to get the text polarity using naï ve Bayes algorithm. This step achieved prediction accuracy results ranging from 72.73% to 86.21%. The second step combines news polarities and historical stock prices together to predict future stock prices. This improved the prediction accuracy up to 89.80%.

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“…In specific, this research follows the main stream of text mining process [4], [8], [12]. The procedure here is quite elementary.…”

Section: Text Miningmentioning

confidence: 99%

Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method

Xie

Jiang

2017

JCP

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News items have significant impact on stock markets but the ways is obscure. Many previous works have aimed at finding accurate stock market forecasting models. In this paper, we use text mining and sentiment analysis on Chinese online financial news, to predict Chinese stock tendency and stock prices based on support vector machine (SVM). Firstly, we collect 2,302,692 news items, which date from 1/1/2008 to 1/1/2015. Secondly, based on this dataset, a specific domain stop-word dictionary and a precise sentiment dictionary are formed. Thirdly, we propose a forecasting model using SVM. On the algorithm of SVM implementation, we also propose two parameter optimization algorithms to search for best initial parameter setting. Result shows that parameter G has the main effect, while parameter C's effect is not obvious. Furthermore, support vector regression (SVR) models for different Chinese stocks are similar whereas in support vector classification (SVC) models best parameters are quite differential. Series of contrast experiments show that: a) News has significant influence on stock market; b) Expansion input vector for additional situations when that day has no news data is better than normal input in SVR, yet is worse in SVC; c) SVR shows a fantastic degree of fitting in predicting stock fluctuation while such result has some time lag; d) News effect time lag for stock market is less than two days; e) In SVC, historic stock data has a most efficient time lag which is about 10 days, whereas in SVR this effect is not obvious. In addition, based on the special structure of the input vector, we also design a method to calculate the financial source impact factor. Result suggests that the news quality and audience number both have significant effect on the source impact factor. Besides, for Chinese investors, traditional media has more influence than digital media.If traversing the whole solution space, there is always a best parameter pair to perform in the model. The different part from the LIBSVM's grid search is the evaluation process. For each (c, g) pair, 10 random train dataset and test dataset groups are picked from database. SCC and MSE value of 10 groups will be averaged for evaluating. In such situation, the average SCC and MSE have no statistic meaning and only serve to the evaluation purpose. The optimum solution plane of traverse result is shown below.

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Analysis of stock market using text mining and natural language processing

Cited by 15 publications

References 2 publications

Predicting Indian Stock Prices through Sentiment Score and Data Mining Techniques

Predicting Indian Stock Prices through Sentiment Score and Data Mining Techniques

Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis

Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method

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