A topic-based sentiment analysis model to predict stock market price movement using Weibo mood

Chen, Wen Hao; Cai, Yi; Lai, Kin Keung; Xie, Haoran

doi:10.3233/web-160345

Cited by 27 publications

(10 citation statements)

References 28 publications

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“…For instance, SA helps marketing managers to understand and monitor customer’s tendencies as well as opinions about products, services or other issues [2]. Governments and political actors are also interested in monitoring citizen’s sentiments about various political issues, their offered services, population health and more [3].…”

Section: Introductionmentioning

confidence: 99%

ASA: A framework for Arabic sentiment analysis

Oussous

Benjelloun

Lahcen

et al. 2019

Journal of Information Science

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Sentiment analysis (SA), also known as opinion mining, is a growing important research area. Generally, it helps to automatically determine if a text expresses a positive, negative or neutral sentiment. It enables to mine the huge increasing resources of shared opinions such as social networks, review sites and blogs. In fact, SA is used by many fields and for various languages such as English and Arabic. However, since Arabic is a highly inflectional and derivational language, it raises many challenges. In fact, SA of Arabic text should handle such complex morphology. To better handle these challenges, we decided to provide the research community and Arabic users with a new efficient framework for Arabic Sentiment Analysis (ASA). Our primary goal is to improve the performance of ASA by exploiting deep learning while varying the preprocessing techniques. For that, we implement and evaluate two deep learning models namely convolutional neural network (CNN) and long short-term memory (LSTM) models. The framework offers various preprocessing techniques for ASA (including stemming, normalisation, tokenization and stop words). As a result of this work, we first provide a new rich and publicly available Arabic corpus called Moroccan Sentiment Analysis Corpus (MSAC). Second, the proposed framework demonstrates improvement in ASA. In fact, the experimental results prove that deep learning models have a better performance for ASA than classical approaches (support vector machines, naive Bayes classifiers and maximum entropy). They also show the key role of morphological features in Arabic Natural Language Processing (NLP).

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Section: Introductionmentioning

confidence: 99%

ASA: A framework for Arabic sentiment analysis

Oussous

Benjelloun

Lahcen

et al. 2019

Journal of Information Science

View full text Add to dashboard Cite

show abstract

“…In addition to research on the stock market in developed countries, scholars have attempted to predict Chinese stocks with local social medias. Chen et al 17 proposed a Weibo emotion mining approach to predict stock market price and noted that financial decisions are significantly driven by emotions. Guo et al collected comments and tweets from the Chinese stock market website and proposed a thermal optimal path to analyze the relationship between investor sentiment and the stock market.…”

Section: Related Workmentioning

confidence: 99%

Stock movement prediction with sentiment analysis based on deep learning networks

Shi

Zheng

Guo

et al. 2020

Concurrency and Computation

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With the development of Internet and big data, it is more convenient for investors to share opinions or have a discuss with others via the web, which creates massive unstructured data. These data reflect investors' emotions and their investment intentions, and it will further affect the movement of the stock market. Although researchers have been attempted to use sentiment information to predict the market, the sentiment features used are driven by outdated emotion extraction systems. In this article, we proposed a new sentiment analysis system with deep neural networks for stock comments and applied estimated sentiment information to the stock movement forecasting. The empirical results showed that our deep sentiment classification method achieved a 9% improvement over the logistic regression algorithm, and provided an accurate sentiment extractor for the next predicting step. In addition our new hybrid features that mix stock trading data and sentiment information achieved 1.25% improvement among 150 Chinese stocks in the testing dataset. For American stocks, the sentiment information would reduced the predicting results. We found that emotion features extracted from comments are indeed effective for stocks with a higher price to book value and a lower beta risk value in China. K E Y W O R D S deep learning, financial comments, sentiment analysis, stock movement prediction 1 INTRODUCTION Sentiment analysis is one of the most important research directions in artificial intelligence and has been applied in many fields, such as stock predicting, political science, and health science. 1,2 Among these applications, stock movement prediction has wide commercial potential and market prospects. However, the fluctuation of the stock market is closely related to the development of the national economy, and the market itself is also a complex nonlinear dynamic system that is susceptible to many factors. 3 Due to the complexity of market rules, the volatility of prices and the diversity of factors also affect the market. Generally, there are approximately two basic methods for stock prediction. The first is a kind of qualitative analysis method in which the effectiveness of forecasts depends largely on the capabilities and experience of experts. The other is the technical analysis method, which contains statistical methods and a data mining algorithm. According to academic studies and practical applications, methods relying solely on expert strategic analysis are insufficient to improve the prediction accuracy. Therefore, researchers have begun to pay more attention to the multisource heterogeneous data and to introduce deep learning methods into this field. In recent decades, the rapid development of the mobile Internet and online applications has brought the whole world into the big data era. It is more convenient for investors to share their opinions or have a discussion with others by pushing messages. People are also likely to browse the web before making investment decisions. All these activities provide multisource ...

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“…Using Twitter as well, Zhang et al found that the sentiment scores can be used to predict NASDAQ and S&P500 index as well (Zhang et al, 2011). Besides, Chen et al demonstrated that public mood states extracted from large-scale posts represent the real public opinions on certain topics related to the stock market (Chen et al, 2016). These topicbased public mood states from Sina Weibo are used to predict the stock price movement.…”

Section: Behavioral Economics and Financementioning

confidence: 99%

Exploring public mood toward commodity markets: a comparative study of user behavior on Sina Weibo and Twitter

Chen

Lai

Cai

2020

INTR

Self Cite

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PurposeSina Weibo and Twitter are the top microblogging platforms with billions of users. Accordingly, these two platforms could be used to understand the public mood. In this paper, the authors want to discuss how to generate and compare the public mood on Sina Weibo and Twitter. The predictive power of the public mood toward commodity markets is discussed, and the authors want to solve the problem that how to choose between Sina Weibo and Twitter when predicting crude oil prices.Design/methodology/approachAn enhanced latent Dirichlet allocation model considering term weights is implemented to generate topics from Sina Weibo and Twitter. Granger causality test and a long short-term memory neural network model are used to demonstrate that the public mood on Sina Weibo and Twitter is correlated with commodity contracts.FindingsBy comparing the topics and the public mood on Sina Weibo and Twitter, the authors find significant differences in user behavior on these two websites. Besides, the authors demonstrate that public mood on Sina Weibo and Twitter is correlated with crude oil contract prices in Shanghai International Energy Exchange and New York Mercantile Exchange, respectively.Originality/valueTwo sentiment analysis methods for Chinese (Sina Weibo) and English (Twitter) posts are introduced, which can be reused for other semantic analysis tasks. Besides, the authors present a prediction model for the practical participants in the commodity markets and introduce a method to choose between Sina Weibo and Twitter for certain prediction tasks.

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A topic-based sentiment analysis model to predict stock market price movement using Weibo mood

Cited by 27 publications

References 28 publications

ASA: A framework for Arabic sentiment analysis

ASA: A framework for Arabic sentiment analysis

Stock movement prediction with sentiment analysis based on deep learning networks

Exploring public mood toward commodity markets: a comparative study of user behavior on Sina Weibo and Twitter

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