Impact of social media sentiments in stock market predictions: A bibliometric analysis

2021 ◽  
pp. 026638212110586
Author(s):  
Deepshi Garg ◽  
Prakash Tiwari
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Stock Market ◽  
Bibliometric Analysis ◽  
Large Set ◽  
Research Papers ◽  
Prolific Author ◽  
Stock Market Prediction ◽  
Scopus Database ◽  
Authorship Analysis

The main objective of the paper is to anticipate a bibliometric analysis of the research on stock market prediction using social media sentiments. The study has taken out a total of 1450 documents from the year Jan 2010 to Dec 2020. This study attempts to identify a significant journal that has maximum documents, most prolific author, most cited papers, countries, institutions, co-authorship network analysis map, inter-country co-authorship network analysis map, and keyword occurrences. The study has used the Scopus database for analyzing the large set of data of research papers that are counted in the study. And the VOSviewer software is used for generating the maps such as co-authorship analysis network map and keyword occurrence network.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals BIBLIOMETRIC ANALYSIS OF PUBLISHED LITERATURE ON TAXATION IN MALAYSIA, BASED ON SCOPUS DATABASE

2021 ◽  
Vol 11 (Number 1) ◽  
pp. 59-86
Author(s):  
Abba Ya’u ◽  
Natrah Saad
Keyword(s):  
Bibliometric Analysis ◽  
Subject Area ◽  
Search Term ◽  
Bibliometric Indicators ◽  
Research Papers ◽  
Research Grants ◽  
Publish Or Perish ◽  
Tax Practitioners ◽  
Scopus Database ◽  
Universiti Utara Malaysia

Taxation research has received considerable attention from many scholars, practitioners and policymakers across the globe. Many scholars have also conducted research on taxation in the Malaysian context. However, papers that track the trends of such research are scanty in the existing literature. The aim of this study is to review the trend and frequencies of published literature on taxation in Malaysia based on the Scopus database using the search term “Malaysia and tax”. The design of the study is bibliometric analysis. As of 23rd September 2020, a total of 88 documents were retrieved and analysed using Excel, Hazing’s Publish or Perish and VOSviewer software. Based on the standard bibliometric indicators, this paper reports the research papers and source types, years and language of publications, subject area, most active institutions, most active sources’ titles, keywords, authorship, abstract, title analysis and citation analysis. Findings revealed that there is an increase in growth rate of literature on studies related to taxation in the Malaysian context from 1977 to 2020 published in the Scopos database. The publications reached an all-time peak in 2016 to 2017 but significantly dropped in 2018 and 2019 based on the data retrieved from the Scopus database. The findings further show that Universiti Teknologi MARA is the most influential institution with 18.18% of the total documents retrieved, followed by Universiti Utara Malaysia with 9.1% respectively. Additional findings of the study show that Advance Science Letters is the highest source title with 14.71% of the published documents. The finding also indicates that Adhikari, Derashid and Zhang (2006) are the most influential authors with 187 citations as at 23rd September 2020. The research is limited to the literatures published in Scopus database, other database were not covered in this study. Malaysian policymakers should provide more research grants to tax practitioners and academicians to increase the level of publications in this field.

scholarly journals Role of Social Sentiment Analysis in Stock Trends Forecasting

2019 ◽  
Vol 8 (2S5) ◽  
pp. 144-147
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Product Reviews ◽  
Stock Market Prediction ◽  
The People ◽  
Market Trends ◽  
Election Results ◽  
Analysis Of Social Networks

Social media like Face book, Twitter have attracted attention from various sectors of study in recent years. Most of the people share ideas, opinions on various topics such as Stock Market Prediction, Digital marketing, Movie review, Election Results Prediction and Product reviews etc,. Forecasting Financial Market is considered to be one of the significant applications of Sentiment Analysis on Social Data like Face book, Twitter. It is essential to accurately predict the movements in stock trends, as the stock market trends are volatile. In the past few years several researches have been carried out for predicting the future trends of stock market through sentiment analysis on social media comments. This paper gives the survey on the various techniques, tools and methodologies adopted by several researchers on Stock Market Prediction based on sentiment analysis of Social networks

scholarly journals Learning-Based Stock Market Trending Analysis by Incorporating Social Media Sentiment Analysis

2021 ◽  
Author(s):  
Zhaoxia Wang ◽  
Zhenda HU ◽  
Fang LI ◽  
Seng-Beng HO
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Prediction Performance ◽  
Support Vector ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Extreme Gradient Boosting ◽  
Media Sentiment

Abstract Stock market trending analysis is one of the key research topics in financial analysis. Various theories once highlighted the non-viability of stock market prediction. With the advent of machine learning and Artificial Intelligence (AI), more and more efforts have been devoted to this research area, and predicting the stock market has been demonstrated to be possible. Learning-based methods have been popularly studied for stock price prediction. However, due to the dynamic nature of the stock market and its non-linearity, stock market prediction is still one of the most dificult tasks. With the rise of social networks, huge amount of data is being generated every day and there is a gaining in popularity of incorporating these data into prediction model in the effort to enhance the prediction performance. Therefore, this paper explores the possibilities of the viability of learning-based stock market trending prediction by incorporating social media sentiment analysis. Six machine learning methods including Multi-Layer Perception, Support Vector Machine, Naïve Bayes, Random Forest, Logistic Regression and Extreme Gradient Boosting are selected as the baseline model. The result indicates the possibilities of successful stock market trending prediction and the performance of different learning-based methods is discussed. It is discovered that the distribution of the value of stocks may affect the prediction performance of the methods involved. This research not only demonstrates the merits and weaknesses of different learning-based methods, but also points out that incorporating social opinion is a right direction for improving the performance of stock market trending prediction.

Bibliometric analysis of the research on photo-electrocatalysis and its possible application to wastewater treatment

2021 ◽  
Vol 02 ◽  
Author(s):  
S. Palmas ◽  
A. Vacca ◽  
M. Mascia ◽  
L. Mais
Keyword(s):  
Wastewater Treatment ◽  
Citation Analysis ◽  
Bibliometric Analysis ◽  
Research Trends ◽  
Future Research ◽  
Environmentally Sustainable ◽  
Data Analyses ◽  
Scopus Database ◽  
Number Of Publications ◽  
Authorship Analysis

Background: Possible applications of photoelectrocatalysis in the field of environmentally sustainable processes have inspired an exponentially increasing number of papers in the last decades. Less frequently, bibliometric analyses are presented, which are especially useful when a considerable amount of data is involved: metadata can be viewed from micro to macro perspective and may suggest ideas for future research. Objective: The objective of this study is to derive information on the research trends and to individuate possible gaps in specific topics, as well information on the authors and their related countries and institutes, and to quantify possible cooperation patterns between them. Methods: In addition to the classic data analyses immediately available on the database, such as the trend of the number of publications per year and the list of the most active authors or countries, the bibliometric analysis has been carried out using the VOSviewer software. Co-authorship and co-citation analyses have been performed, as well as co-occurrence of the keywords explicitly indicated by the authors. Results and Conclusion: Based on the publications in the SCOPUS database, the present work analysed the metadata relating to scientific articles published in the last two decades, dedicated to the research on photo-electrocatalytic processes and their possible application on wastewater treatment. Based on the list of the references present in the examined papers, the co-authorship analysis and the co-citation analysis also allowed the identification of the authors and the publications which have been most influential on the research on the examined topics. Indication has been obtained on the trend of the main topics investigated in time, as well as the current gaps in terms of content-wise and geographic cooperation.

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