Stock Market Prediction Using Twitter Sentiment Analysis

Padmanayana,; Varsha,; Bhavya, K. R.

doi:10.32628/cseit217475

Cited by 17 publications

(19 citation statements)

References 8 publications

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“…Researchers have used social media data for a wide range of purposes-from predicting postpartum depression from Facebook posts (De Choudhury et al, 2014) to trying to predict movements in the stock market based on the sentiment of Tweets (Mittal & Goel, 2010). However, not all uses of social media data have been welcomed by users or seen as acceptable in the research community.…”

Section: Public Data Usementioning

confidence: 99%

Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics

Proferes

Jones

Gilbert

et al. 2021

Social Media + Society

215

146

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This article offers a systematic analysis of 727 manuscripts that used Reddit as a data source, published between 2010 and 2020. Our analysis reveals the increasing growth in use of Reddit as a data source, the range of disciplines this research is occurring in, how researchers are getting access to Reddit data, the characteristics of the datasets researchers are using, the subreddits and topics being studied, the kinds of analysis and methods researchers are engaging in, and the emerging ethical questions of research in this space. We discuss how researchers need to consider the impact of Reddit’s algorithms, affordances, and generalizability of the scientific knowledge produced using Reddit data, as well as the potential ethical dimensions of research that draws data from subreddits with potentially sensitive populations.

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Section: Public Data Usementioning

confidence: 99%

Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics

Proferes

Jones

Gilbert

et al. 2021

Social Media + Society

215

146

View full text Add to dashboard Cite

show abstract

“…These studies targeted US stock market and took all kinds of tweets together. Sannakki and Sambrekar (2021) also were able to find a statistically significant relationship between changes in daily stock price and tweets polarity. They made sentiment analysis on tweets to polarize them into two groups.…”

Section: Literature Reviewmentioning

confidence: 85%

“…Also, they observed that the effect of negative sentiment lasts more than the positive one. Padmanayana et al (2021) achieved 89.8% accuracy in predicting stock prices with sentimental analysis. They collected tweets about sixteen companies and fed these tweets into a machine learning model to classify them into three labels and generate prediction value which was then correlated with stock values.…”

Section: Literature Reviewmentioning

confidence: 99%

Relationship between Twitter activity and stock performance: evidence from Turkish airline industry

Ismayil

Demir

2023

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Purpose The purpose of this paper is to analyze the correlation between the Twitter activity of two airline companies and their stock performance at the Istanbul Stock Exchange (BIST). Design/methodology/approach Overall, 113,018 tweets were divided into 34,152 semantic and 78,866 share tweets. Semantic tweets are tweets mentioning company’s products or services and were labeled manually and with deep learning models. Share tweets were divided into 13,618 relevant and 65,248 irrelevant tweets. Findings A positive correlation was found between share tweets and stock performance. Semantic tweets did not display a correlation with stock performance. Relevant share tweets displayed as a strong correlation as all share tweets for one company. Also, the manual labeling of 8,000 tweets led to the discovery of many insights related to service provision in the airway industry, management of digital support channels, management of reputation on social media and using Twitter as a customer support platform. Practical implications Relevant share tweets comprise only 20% of all share tweets for one company and show the same level of correlation with stock performance. This means that the efficiency of business intelligence solutions created to monitor Twitter activity can be improved five times by saving computational power, network bandwidth and data storage. Originality/value Previous research has analyzed all Twitter activity taken together. By dividing tweets into semantic and share tweets, this paper illustrates that it is, in fact, share tweets that are correlated with stock performance and not semantic tweets.

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“…Twitter data reveals how companies are responding to macromarketing changes like sustainability. Twitter data has been used to analyze a myriad of topics, such as financial issues (Daniel et al, 2017), stock performance (Mittal and Goel, 2012), political campaigns (Budiharto and Meiliana, 2018), sentiments towards disease (Gabarron et al, 2019), branding (Ghiassi et al, 2013), COVID-19 (Rahman et al, 2021) and more.…”

Section: Methodsmentioning

confidence: 99%

Social media branding in the food industry: comparing B2B and B2C companies’ use of sustainability messaging on Twitter

Garner

Mady

2023

JBIM

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Purpose Supply chains are under increased scrutiny as consumers have become aware of the dark side of downstream production practices. Many articles and exposés have led consumers to be more conscious of purchasing products from companies who source materials in a socially responsible and ethical manner. As a result, business-to-business (B2B) and business-to-consumers (B2C) companies are under increased pressure to source raw materials in a transparent and ethical way. Because of the associated costs, companies then look to benefit from increased brand equity by promoting to consumers how ethical their products are. The purpose of this study is to look at the case of the food industry to analyze sustainability messaging on Twitter in both B2B and B2C companies to determine which of the dimensions of sustainability (people, profit, planet) are being emphasized. Design/methodology/approach In this study, two published dictionaries were combined to capture the three dimensions of “sustainability,” and these scales were then used to analyze Twitter posts. This study created a unique software package to classify, mine, collect and analyze Twitter data. This study used these tools to analyze 246,386 Twitter posts within a sample of 39 leading B2C and B2B food companies over a 10-year period (2012–2021) to explore brand messaging and engagement rate. Findings This research revealed several interesting results. These include how B2B companies have emphasized the employee (people) dimensions of sustainability, while B2C companies have had a more balanced approach that overall has prioritized the economic dimension (profit) of sustainability. The data on audience engagement revealed a mismatch between the types of sustainability messaging B2B companies and B2C companies in the food industry are posting and what engages audiences. Originality/value This study fills several gaps, including analyzing how B2B and B2C companies use sustainability language in their social media brand management, as well as looking at which dimensions of sustainability they emphasize and which ones engage audiences the most. This research is also novel in combining multiple existing scales under one project to analyze the triple bottom line in the analysis of qualitative texts.

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Stock Market Prediction Using Twitter Sentiment Analysis

Cited by 17 publications

References 8 publications

Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics

Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics

Relationship between Twitter activity and stock performance: evidence from Turkish airline industry

Social media branding in the food industry: comparing B2B and B2C companies’ use of sustainability messaging on Twitter

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