Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction
Published 1 January 2015Open access
Thien Hai Nguyen, Kiyoaki Shirai
Citations250
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The results show that incorporation of the sentiment information from social media can help to improve the stock prediction.
Abstract
Thien Hai Nguyen, Kiyoaki Shirai. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.
Keywords
Computer ScienceDecision Sciences
Journal of Machine Learning ResearchLatent dirichlet allocation
27,049 Citations2003David M. Blei, Andrew Y. Ng +1 more
Lecture notes in computer scienceText categorization with Support Vector Machines: Learning with many relevant features
7,925 Citations1998Thorsten Joachims
SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of di-erent learning tasks, eliminating the need for manual parameter tuning.
The Stanford CoreNLP Natural Language Processing Toolkit
7,238 Citations2014Christopher D. Manning, Mihai Surdeanu +4 more
The design and use of the Stanford CoreNLP toolkit is described, an extensible pipeline that provides core natural language analysis, and it is suggested that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.
now publishers, Inc. eBooksOpinion Mining and Sentiment Analysis
6,745 Citations2008Bo Pang, Lillian Lee
Journal of Computational ScienceTwitter mood predicts the stock market
4,995 Citations2011Johan Bollen, Huina Mao +1 more
This work investigates whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time and indicates that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others.
SSRN Electronic JournalThe Adjustment of Stock Prices to New Information
3,126 Citations2003Eugene F. Fama, Lawrence Fisher +2 more
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining.
2,652 Citations2010Stefano Baccianella, Andrea Esuli +1 more
This work discusses SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications, and reports on the improvements concerning aspect (b) that it embodies with respect to version 1.0.
The Journal of FinanceIs All That Talk Just Noise? The Information Content of Internet Stock Message Boards
2,400 Citations2004Werner Antweiler, Murray Z. Frank
A Survey of Opinion Mining and Sentiment Analysis
1,331 Citations2012Bing Liu, Lei Zhang
Sentiment analysis or opinion mining is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and their attributes.
Joint sentiment/topic model for sentiment analysis
972 Citations2009Chenghua Lin, Yulan He
A novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text is proposed, which is fully unsupervised.
Aspect and sentiment unification model for online review analysis
772 Citations2011Yohan Jo, Alice Oh
This paper proposes Sentence-LDA and extends it to Aspect and Sentiment Unification Model (ASUM), which incorporates aspect and sentiment together to model sentiments toward different aspects and shows that ASUM outperforms other generative models and comes close to supervised classification methods.
ACM Transactions on Information SystemsTextual analysis of stock market prediction using breaking financial news
770 Citations2009Robert P. Schumaker, Hsinchun Chen
This research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: bag of words, noun phrases, and named entities and found that a Proper Noun scheme performs better than the de facto standard of Bag of Words in all three metrics.
Procedia - Social and Behavioral SciencesPredicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”
625 Citations2011Xue Zhang, Hauke Fuehres +1 more
Early work trying to predict stock market indicators such as Dow Jones, NASDAQ and S&P 500 by analyzing Twitter posts found that emotional tweet percentage significantly negatively correlated with Dow Jones, NASDAQ and S&P 500, but displayed significant positive correlation to VIX.
Singapore Management University Institutional Knowledge (InK) (Singapore Management University)Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid
414 Citations2010Xin Zhao, Jing Jiang +2 more
This paper proposes a MaxEnt-LDA hybrid model to jointly discover both aspects and aspect-specific opinion words and shows that with a relatively small amount of training data, this model can effectively identify aspect and opinion words simultaneously.
Financial Analysts JournalNews or Noise? Internet Postings and Stock Prices
402 Citations2001Robert Tumarkin, Robert Whitelaw
Journal of Management Information SystemsAn Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks
240 Citations2001Steven Walczak
It is shown that those neural networks-given an appropriate amount of historical knowledge-can forecast future currency exchange rates with 60 percent accuracy, while those neural Networks trained on a larger training set have a worse forecasting performance.
Applied IntelligenceStock market prediction with multiple classifiers
236 Citations2006Bo Qian, Khaled Rasheed
This paper investigated the predictability of the Dow Jones Industrial Average index to show that not all periods are equally random, and used the Hurst exponent to select a period with great predictability.
Exploiting Topic based Twitter Sentiment for Stock Prediction
216 Citations2013Jianfeng Si, Arjun Mukherjee +4 more
This paper proposes a technique to leverage topic based sentiments from Twitter to help predict the stock market by utilizing a con- tinuous Dirichlet Process Mixture model to learn the daily topic set and regress the stock index and the Twitter sentiment time series to predict the market.
Information Processing & ManagementA quantitative stock prediction system based on financial news
205 Citations2009Robert P. Schumaker, Hsinchun Chen
This paper examines the problem of discrete stock price prediction using a synthesis of linguistic, financial and statistical techniques to create the Arizona Financial Text System (AZFinText), and finds that stocks partitioned by Sectors were most predictable in measures of Closeness, Mean Squared Error (MSE) score and Simulated Trading return.
Exploiting Coherence for the Simultaneous Discovery of Latent Facets and associated Sentiments
105 Citations2011Himabindu Lakkaraju, Chiranjib Bhattacharyya +2 more
This work is the first attempt to combine the notions of syntactic and semantic dependencies in the domain of review mining by jointly discovering latent facets and sentiment topics, and also order the sentiment topics with respect to a multi-point scale, in a language and domain independent manner.
Expert Systems with ApplicationsStock price forecast using Bayesian network
99 Citations2012Yi Zuo, Eisuke Kita
The results show that the forecast accuracy of the present algorithm is better than that of the traditional time-series forecast algorithms in comparison of their correlation coefficient and the root mean square error.
Open Research Online (The Open University)An Experiment in Integrating Sentiment Features for Tech Stock Prediction in Twitter
90 Citations2012Tien Thanh Vu, Shu Chang +2 more
A novel model combining features namely positive and negative sentiment, consumer confidence in the product with respect to ‘bullish’ or ‘bearish” lexicon and three previous stock market movement days is proposed.
SSRN Electronic JournalSemantic Frames to Predict Stock Price Movement
83 Citations2013Boyi Xie, Rebecca J. Passonneau +2 more
This work introduces a novel tree representation, and uses it to train predictive models with tree kernels using support vector machines, and shows that features derived from semantic frame parsing have significantly better performance across years on the polarity task.
Engineering Management ResearchUp/Down Analysis of Stock Index by Using Bayesian Network
36 Citations2012Yi Zuo, Eisuke Kita
The present algorithm is applied for predicting the up/down analysis of the daily stock indeies in 2007 and the results are compared with the traditional algorithms; Psychological line and trend estimation, which are popular algorithms which are well-known by the traders.
Lecture notes in computer scienceText Classification of Technical Papers Based on Text Segmentation
27 Citations2013Thien Hai Nguyen, Kiyoaki Shirai
A new model for text classification based on the structure of papers, called Back-off model, which achieves 60.45% Exact Match Ratio and 68.75% F-measure was proposed and it was shown that Back- off model outperformed two existing methods, ML-kNN and Binary Approach.
Algorithmic FinanceStock chatter: Using stock sentiment to predict price direction
26 Citations2013Michael Rechenthin, W. Nick Street +1 more
Examination of a popular stock message board finds slight daily predictability using supervised learning algorithms when combining daily sentiment with historical price information and questions if the existence of dishonest posters are capitalizing on the popularity of the boards by writing sentiment in line with their trading goals as a means of influencing others, and therefore undermining the purpose of the board.
Lecture notes in computer scienceAspect-Based Sentiment Analysis Using Tree Kernel Based Relation Extraction
17 Citations2015Thien Hai Nguyen, Kiyoaki Shirai
The authors' model can identify polarity of a given aspect based on the aspect-opinion relation extraction and outperformed the model without relation extraction by 5.8% on accuracy and 4.6% on F-measure.