Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market

Nasseri, Alya Al; Tucker, Allan; Cesare, Sergio de

doi:10.1007/978-3-319-11812-3_2

Cited by 9 publications

(9 citation statements)

References 18 publications

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“…For the aim of the exploratory analysis, the information in a tweet can be divided into three main blocks: (i) general information about the tweet such as the ID, the language, the number of retweets and favorites 1 and especially the body of the tweet (ii) geolocation where the tweet was sent from and (iii) user information such as name, description, followers, friends, number of favorite tweets, number of retweets, account location, language, if it is a verified account, and graphic representation of the account.…”

Section: A Tweet Structurementioning

confidence: 99%

“…Currently, Twitter is one of the most used platforms to share financial information from companies, brokers, news agencies or individual investors. Above other financial information sources like message boards or discussion forums, stock microblogging exhibit three distinctive characteristics [33]: (1) Twitter's public timeline may capture the natural market conversation more accurately and reflect up to date developments; (2) Twitter supports a more ticker-like live conversation, which allows twitter-microbloggers to be exposed to the most recent information of all stocks and does not require users to actively enter the forum for a particular stock; and (3) twitter-microbloggers should have a stronger incentive to publish valuable information in order to maintain reputation (increase mentions, the rate of retweets and their followers), while financial bloggers can be indifferent to their reputation in the forum. The combinations of this stream of information with suitable processing and analysis techniques would support the action for many financial stakeholders and even law enforcement agencies.…”

Section: Introductionmentioning

confidence: 99%

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The Irruption of Cryptocurrencies Into Twitter Cashtags: A Classifying Solution

2020

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There is a consensus about the good sensing characteristics of Twitter to mine and uncover knowledge in financial markets, being considered a relevant feeder for taking decisions about buying or holding stock shares and even for detecting stock manipulation. Although Twitter hashtags allow to aggregate topic-related content, a specific mechanism for financial information also exists: Cashtag (consisting of the company ticker preceded by $) is a supporting mechanism to track financial tweets referring to a company listed in a stock market. However, according to our experiments and due to the lack of conventions in cashtags usage, the irruption of cryptocurrencies has resulted in a significant degradation on the cashtag-based aggregation of posts. Unfortunately, Twitter' users may use homonym tickers to refer to cryptocurrencies and to companies in stock markets, which means that filtering by cashtag may result on both posts referring to stock companies and cryptocurrencies. This research proposes automated classifiers to distinguish conflicting cashtags and, so, their container tweets by analyzing the distinctive features of tweets referring to stock companies and cryptocurrencies. As experiment, this paper analyses the interference between cryptocurrencies and company tickers in the London Stock Exchange (LSE), specifically, companies in the main and alternative market indices FTSE-100 and AIM-100. Heuristic-based as well as supervised classifiers are proposed and their advantages and drawbacks, including their ability to self-adapt to Twitter usage changes, are discussed. The experiment confirms a significant distortion in collected data when colliding or homonym cashtags exist, i.e., the same $ acronym to refer to company tickers and cryptocurrencies. According to our results, the distinctive features of posts including cryptocurrencies or company tickers support accurate classification of colliding tweets (homonym cashtags) and Independent Models, as the most detached classifiers from training data, have the potential to be trans-applicability (in different stock markets) while retaining performance. INDEX TERMS AIM-100, cashtags, cryptocurrencies, data analysis, FTSE-100, london stock exchange, support vector machines, twitter.

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Section: A Tweet Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Irruption of Cryptocurrencies Into Twitter Cashtags: A Classifying Solution

2020

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“…Zhang [58] found out a high negative correlation between mood states like hope, fear and worry in tweets with the Dow Jones Average Index. Therefore, text based sentiment was considered useful to make trading decisions [2] or predict useful stock market variable, [13,28,36,41]. Recently, [16] investigated the correlation of sentiments of public with stock increase and decreases using Pearson correlation coefficient for stock.…”

Section: Twitter As a Source For Decision Makingmentioning

confidence: 99%

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

Vilas

Redondo

Crockett

et al. 2018

Multimed Tools Appl

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There is a general consensus of the good sensing and novelty characteristics of Twitter as an information media for the complex financial market. This paper investigates the permeability of Twittersphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to financial-specific events and establishes Twitter as a relevant feeder for taking decisions regarding the financial market and event fraudulent activities in that market. However, the provenance of contributions, their different levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specific financial action on the 27th January 2017: the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange

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“…Researcher measuring microblogging sentiment investors with text classifier technology [32], lexicalsemantic extension and correlation analysis method [24], naive Bayesian classification method [10], [18], Sprenger et al, 2014), text mining [46], [47], combines text-mining, feature selection and Bayesian networks [48], IBM natural language processing [49], machine learning algorithms to infer the relationship between the general public view regarding a stock and its evolution within the stock market [50].…”

Section: Introductionmentioning

confidence: 99%

Defining and Measuring Microblogging Sentiment Investors on Stock Market: A Literature Review

Fariska¹,

Nugraha²,

Rohandi³

2021

Proceedings of the 5th Global Conference on Business, Management and Entrepreneurship (GCBME 2020)

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The purpose of this paper is to review microblogging sentiment investors on a literature-based. Focusing on defining and measuring online sentiment investors and understanding their impact on financial market behavior-using scholarly articles to analyze, define, and measure microblogging sentiment investors. All the articles have been classified to measure and underpinnings online investor sentiment theory and other determinants in psychological measure. We have used citation and bibliographic coupling analysis to provide a structure of microblogging sentiment investors. This research also investigates how microblogging forums have become an online platform for exchanging stock information among investors. This paper's result defines investor sentiment research development will further reveal new ways of measuring investor sentiment through big data social media on microblogging sentiment investors construct by identifying authors, cite, references, or keywords.

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Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market

Cited by 9 publications

References 18 publications

The Irruption of Cryptocurrencies Into Twitter Cashtags: A Classifying Solution

The Irruption of Cryptocurrencies Into Twitter Cashtags: A Classifying Solution

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

Defining and Measuring Microblogging Sentiment Investors on Stock Market: A Literature Review

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