Sentiment analysis to predict Bombay stock exchange using artificial neural network

Khatri, Sunil Kumar; Singhal, Himanshu; Johri, Prashant

doi:10.1109/icrito.2014.7014714

Cited by 13 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A couple of studies have also applied text mining techniques to data extracted from the social media with a view to generating meaning insights for economic predictions. These include studies on the role of twitter sentiment in the determination of stock price returns (Mittal and Goel, 2012;Sumbureru, 2013;Khatri et al, 2014;Ranco et al, 2015;and Kumar et al, 2015), social media sentiment and consumer confidence (Daas and Puts, 2014); sentiment indicators and exchange rate prediction (Hopper, 1997;Crone and Koeppel, 2014;Janetzko, 2014;Ozturk and Ciftci, 2014;Plakandaras et. al., 2015;Iwantoro and Koesrindartoto, 2017), amongst others.…”

Section: Review Of Literaturementioning

confidence: 99%

A Text Mining Analysis of Central Bank Monetary Policy Communication in Nigeria

Tumala¹,

Omotosho²

2020

CBN-JAS

View full text Add to dashboard Cite

This paper employs text-mining techniques to analyse the communication strategy of the Central Bank of Nigeria (CBN) during the period 2004-2019. Since the policy communique released after each meeting of the CBN’s monetary policy committee (MPC) represents an important tool of central bank communication, we construct a corpus based on 87 policy communiques with a total of 123, 353 words. Having processed the textual data into a form suitable for analysis, we examined the readability, sentiments, and topics of the policy documents. While the CBN’s communication has increased substantially over the years, implying increased monetary policy transparency; the computed Coleman and Liau readability index shows that the word and sentence structures of the policy communiques have become more complex, thus reducing its readability. In terms of monetary policy sentiments, we find an average net score of -10.5 per cent, reflecting the level of policy uncertainties faced by the MPC over the sample period. In addition, our results indicate that the topics driving the linguistic contents of the communiques were influenced by the Bank’s policy objectives as well as the nature of shocks hitting the economy per period.

show abstract

Section: Review Of Literaturementioning

confidence: 99%

A Text Mining Analysis of Central Bank Monetary Policy Communication in Nigeria

Tumala¹,

Omotosho²

2020

CBN-JAS

View full text Add to dashboard Cite

show abstract

“…They found that the terms happy and trouble were commonly used in the emotional word database, and when the Twitter post contained stock market-related words, the average emotional value of the tweets changed from happiness to unhappiness by approximately 45% [9]. In a further study, Khatri, Singhal, and Johri [12] tried to train the neural network with words such as happy, hope, sad, and disappointing as input to predict the Bombay Exchange index. Their study indicated that an artificial neural network provided optimum results when set up with one hidden layer containing nine neurons [9].…”

Section: Related Workmentioning

confidence: 99%

Tuning of Customer Relationship Management (CRM) via Customer Experience Management (CEM) using Sentiment Analysis on Aspects Level

AL-Rubaiee¹,

Alomar²,

Qiu³

et al. 2018

ijacsa

View full text Add to dashboard Cite

This study proposes a framework that combines a supervised machine learning and a semantic orientation approach to tune Customer Relationship Management (CRM) via Customer Experience Management (CEM). The framework extracts data from social media first and then integrates CRM and CEM by tuning and optimising CRM to reflect the needs and expectations of users on social media. In other words, in order to reduce the gap between the users' predicted opinions in CRM and their opinions on social media, the existing data from CEM will be applied to determine the similar behavioural patterns of customers towards similar outcomes within CRM. CRM data and extracted data from social media will be consolidated by the unsupervised data mining method (association). The framework will lead to a quantitative approach to uncover relationships between the extracted data from social media and the CRM data. The results show that changing some aspects of the e-learning criteria that were required by students in their social media posts can help to enhance the classification accuracy in the learning management system (LMS) data and to understand more students' studying statuses. Furthermore, the results show matching between students' opinions in CRM and CEM, especially in the negative and neutral classes.

show abstract

“…A technical analysis is used to find the price of the stock in a period. Mathematical methods are used in the technical analysis to indicate the buy/sell signals [2], [3]. Fundamental analysis is the technique to analyze the company performance by calculating profit and liabilities.…”

Section: Introductionmentioning

confidence: 99%

“…Sunil Kumar Khatri et al [3] have proposed a sentiment analysis to predict Bombay stock exchange using ANN. Many forums are there for the investors to participate and share their views related to the market.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy based Soft Computing Technique to Predict the Movement of the Price of a Stock

Dutta¹

2018

ijacsa

View full text Add to dashboard Cite

Abstract-Soft computing is a part of an artificial intelligence, and fuzzy logic is the study of fuzziness on data. The combination of these two techniques can provide an intelligent system with more ability and flexibility. The nature of data in the stock/capital market is more complex and challenging to predict the movement of the price of the stock. The study has combined both fuzzy c-means and neural network technique for the prediction of the price of the stock. The research finds an optimum solution to predict the future price of a stock. The comparison of time and space complexity proved that the proposed method is better than the existing methods.

show abstract

Sentiment analysis to predict Bombay stock exchange using artificial neural network

Cited by 13 publications

References 3 publications

A Text Mining Analysis of Central Bank Monetary Policy Communication in Nigeria

A Text Mining Analysis of Central Bank Monetary Policy Communication in Nigeria

Tuning of Customer Relationship Management (CRM) via Customer Experience Management (CEM) using Sentiment Analysis on Aspects Level

A Fuzzy based Soft Computing Technique to Predict the Movement of the Price of a Stock

Contact Info

Product

Resources

About