Real-Time Sentiment Analysis of Twitter Streaming data for Stock Prediction

Das, Sushree; Behera, Ranjan Kumar; Kumar, Mukesh; Rath, Santanu Kumar

doi:10.1016/j.procs.2018.05.111

Cited by 97 publications

(42 citation statements)

References 13 publications

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“…al. [118] implemented sentiment analysis on Twitter posts along with the stock data for price forecasting using RNN. Similarly, the authors of [119] used sentiment classification (neutral, positive, negative) for the stock open or close price prediction with various LSTM models.…”

Section: Stock Price Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

795

294

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Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.

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Section: Stock Price Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

795

294

View full text Add to dashboard Cite

show abstract

“…[14] in 2017 and Das et al . [15] in 2018 have both used long short-term (LSTM) recurrent neural networks, respectively on general indicators and sentiment analysis. In addition, Ding et al .…”

Section: Related Workmentioning

confidence: 99%

Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies

Dong¹

2019

PLoS ONE

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Stock trend prediction is a challenging task due to the market’s noise, and machine learning techniques have recently been successful in coping with this challenge. In this research, we create a novel framework for stock prediction, Dynamic Advisor-Based Ensemble (dynABE). dynABE explores domain-specific areas based on the companies of interest, diversifies the feature set by creating different “advisors” that each handles a different area, follows an effective model ensemble procedure for each advisor, and combines the advisors together in a second-level ensemble through an online update strategy we developed. dynABE is able to adapt to price pattern changes of the market during the active trading period robustly, without needing to retrain the entire model. We test dynABE on three cobalt-related companies, and it achieves the best-case misclassification error of 31.12% and an annualized absolute return of 359.55% with zero maximum drawdown. dynABE also consistently outperforms the baseline models of support vector machine, neural network, and random forest in all case studies.

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“…Existing opinion annotations schemes (i.e., OpinionMining-ML, EmotionML and SentiML) fail to deal with many situations which, if annotated well, could be influential for developing better opinion mining systems. Problems like contextual ambiguities [6,7], lack of semantics interpretation on sentence level, tackling temporal expressions [8,9], identification of opinion holders [10][11][12], opinion aggregation and their comparison [13,14] remain unanswered by these annotations. Each of the opinion annotation schemes have positive and negative features associated with them but there is a need to have a strong opinion annotation which combines positive features of existing schemes (like flexible emotion vocabulary choice in EmotionML, feature-level processing of OpinionMining-ML, etc.)…”

Section: Motivation and Contributionmentioning

confidence: 99%

OpinionML—Opinion Markup Language for Sentiment Representation

Attik¹,

Missen

Coustaty

et al. 2019

Symmetry

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It is the age of the social web, where people express themselves by giving their opinions about various issues, from their personal life to the world’s political issues. This process generates a lot of opinion data on the web that can be processed for valuable information, and therefore, semantic annotation of opinions becomes an important task. Unfortunately, existing opinion annotation schemes have failed to satisfy annotation challenges and cannot even adhere to the basic definition of opinion. Opinion holders, topical features and temporal expressions are major components of an opinion that remain ignored in existing annotation schemes. In this work, we propose OpinionML, a new Markup Language, that aims to compensate for the issues that existing typical opinion markup languages fail to resolve. We present a detailed discussion about existing annotation schemes and their associated problems. We argue that OpinionML is more robust, flexible and easier for annotating opinion data. Its modular approach while implementing a logical model provides us with a flexible and easier model of annotation. OpinionML can be considered a step towards “information symmetry”. It is an effort for consistent sentiment annotations across the research community. We perform experiments to prove robustness of the proposed OpinionML and the results demonstrate its capability of retrieving significant components of opinion segments. We also propose OpinionML ontology in an effort to make OpinionML more inter-operable. The ontology proposed is more complete than existing opinion ontologies like Marl and Onyx. A comprehensive comparison of the proposed ontology with existing sentiment ontologies Marl and Onyx proves its worth.

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Real-Time Sentiment Analysis of Twitter Streaming data for Stock Prediction

Cited by 97 publications

References 13 publications

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies

OpinionML—Opinion Markup Language for Sentiment Representation

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