Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”

Zhang, Xue; Fuehres, Hauke; Gloor, Peter A.

doi:10.1016/j.sbspro.2011.10.562

Cited by 496 publications

(303 citation statements)

References 13 publications

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“…For instance, Choudhury et al (2008) describe promising results when exploiting content from weblogs to predict stock market movements. Other researchers (e.g., Gilbert and Karahalios, 2010;Bollen et al, 2011a and2011b;or Zhang et al, 2011) have shown that public mood measured through social media (e.g., LiveJournal or Twitter) is somewhat related to stock market movements: for instance, anxiety or fear tend to predate downward movements in the markets. It must be noted, however, that it has not been demonstrated that such emotional indices can predict stock returns.…”

Section: Stock Market Movementmentioning

confidence: 99%

The power of prediction with social media

et al. 2013

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Social media today provide an impressive amount of data about users and their societal interactions, thereby offering computer and social scientists, economists, and statisticiansamong others-many new opportunities for research exploration. Arguably, one of the most interesting lines of work is that of predicting future events and developments based on social media data, as we have recently seen in the areas of politics, finance, entertainment, market demands, health, etc. In fact, an average of one in seven research papers presented at the WWW, But what can be successfully predicted and why? Since the first algorithms and techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains.Better understanding the predictive power and limitations of social media is therefore of utmost importance, in order to be successful and avoid false expectations, misinformation or unintended consequences. Today, current methods and techniques are far from being well understood, and it is mostly unclear to what extent or under what conditions the different methods for prediction can be applied to social media. While there exists a respectable and growing amount of literature in this area, current work is fragmented, characterized by a lack of commonly accepted evaluation approaches. Yet, this research seems to have reached a sufficient level of interest and relevance to justify a dedicated section.This special section aims to shape a frame of important questions to be addressed in this field, and fill the gaps in current research with presentations of early research on algorithms, techniques, methods and empirical studies aimed at the prediction of future or current events based on user-generated content in social media.2

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Section: Stock Market Movementmentioning

confidence: 99%

The power of prediction with social media

et al. 2013

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“…Their works were fo llo wed by Zhang, et al [15] who tried to predict the DJIA, NASDAQ, S&P 500, and VIX by analyzing Twitter posts . Rao and Srivastava [16] who got an 88% accuracy based on their sentiment analysis using Twitter.…”

Section: Related Workmentioning

confidence: 99%

Prediction Model of the Stock Market Index Using Twitter Sentiment Analysis

Caliñgo¹,

Sison²,

Tanguilig³

2016

IJITCS

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Abstract-Stock market pred iction has been an interesting research topic for many years. Finding an efficient and effective means of predicting the stock market found its way in different social networking platforms such as Twitter. Studies have shown that public moods and sentiments can affect one's opinion. This study explored the tweets of the Filipino public and its possible effects on the movement of the closing Index of the Philippine Stock Exchange. Sentiment Analysis was used in p rocessing individual t weets and determining its polarity -either positive or negative. Tweets were given a positive and negative probability scores depending on the features that matched the trained classifier. Granger causality testing identified whether or not the past values of the Twitter t ime series were useful in pred icting the future price of the PSE Index. Two predict ion models were created based on the p-values and regression algorith ms. The results suggested that the tweets collected using geo location and local news sources proved to be causative of the future values of the Philippine Stock Exchange closing Index.

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“…Sentiment analysis of text has been tested and found to carry some signal in several recent reports [20,24,21,30,3,2]. The hypothesis in these cases is one or a combination of the following:…”

Section: Investment Trendsmentioning

confidence: 99%

Usefulness of Sentiment Analysis

Karlgren

Sahlgren

Olsson

et al. 2012

Lecture Notes in Computer Science

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Abstract.What can text sentiment analysis technology be used for, and does a more usage-informed view on sentiment analysis pose new requirements on technology development?1 Human emotion, attitude, mood, affect, sentiment, opinion, and appealAnalysis of sentiment in text is a new and rapidly growing field of study and application. This paper outlines some application areas for sentiment analysis technology, and discusses what requirements a technology for sentiment analysis of text should be able to answer to. The human sensations of emotion, attitude, mood, affect, sentiment, opinion, and appeal all contribute to the basic categories of sentiment analysis of text, but they have been studied in their own right for a long time. Traditionally, this has been done in the behavioural sciences; [9] but today also by information technologists, especially with respect to interaction design . "Emotion" , "attitude" , "mood" "affect", "sentiment" , and "appeal" are everyday words. No consensus beyond the general vernacular usage of the most common terms can currently be assumed, but mostly the usage tends to hold that affect or affective state is the more general term, emotion a momentary, mostly conscious sensation, and mood an affective frame over a longer time span, not necessarily consciously acknowledged by its holder. These affective aspects of human behaviour and information processing are studied in various ways with variously differing perspectives, but the assumptions of most researchers is that people are in continuously changing affective states of some sort; and that activities people engage in have emotional impact and that their decision making, behaviour, and performance are informed by the affective state of the user. This appears to be true even for very mundane tasks such as workplace tasks or accessing information items, but most importantly for the purposes of this paper, in producing and understanding information items, and, it is assumed, even to the extent that mood, with respect to some topic or facet of life, will colour and influence the understanding, generation, or processing of information on another quite different topic.Sentiment analysis of text typically assumes that lexical items found in the text carry attitudinal loading. Previous work on the loading of individual features and the affective reaction of human subjects to linguistic items on the level of words and terms [16] or still images [15] quite often take "emotion labels" to be

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Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”

Cited by 496 publications

References 13 publications

The power of prediction with social media

The power of prediction with social media

Prediction Model of the Stock Market Index Using Twitter Sentiment Analysis

Usefulness of Sentiment Analysis

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