Lexicon-based Comments-oriented News Sentiment Analyzer system

Moreo, Alejandro; Romero, Margarita Sánchez; Castro, Juan Luis; Zurita, J. M.

doi:10.1016/j.eswa.2012.02.057

Cited by 121 publications

(45 citation statements)

References 38 publications

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“…Medhat et al conducted an extensive survey of current sentiment analysis algorithms and found that the lexicon-based approach has been more often used in research studies from 2010 to 2013 (Medhat, Hassan, & Korashy, 2014). In addition, Moreo et al proposed a lexicon-based news sentiment analyzer that can incorporate non-standard language and generate sentiment measures based on specific topics of interest (Moreo, Romero, Castro, & Zurita, 2012). A study conducted by Schumaker et al investigated the effectiveness of the Arizona Financial Text system, which leverages the use of OpinionFinder for identifying the text's tone and polarity (Schumaker, Zhang, Huang, & Chen, 2012).…”

Section: News Sentimentmentioning

confidence: 99%

News Sentiment to Market Impact and its Feedback Effect

Liu

Yang

2015

SSRN Journal

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Although market feedback on investor sentiment effect has been conceptually identified in the existing finance literature, and investment strategies have been designed to explore this effect, there lacks systematic analysis in a quantified manner on such effect. Digitization of news articles and the advancement of computational intelligence applications have led to a growing influence of news sentiment over financial markets in recent years. News sentiment has often been used as a proxy for gauging investor sentiment and reflecting the aggregate confidence of the society toward future market. Previous studies have primarily focused on elucidating the unidirectional impact of news sentiment on market returns and not vice versa. In this study, we analyze more than 12 millions of news articles and document the presence of a significant feedback effect between news sentiment and market returns across the major indices in the U.S. financial market. More specifically, we find that news sentiment exhibits a lag-5 effect on market returns and conversely market returns elicit consistent lag-1 effects on news sentiment. This aligns well with our intuition that news sentiment drives trading activity and investment decisions. In turn, heightened investment activity further stimulates involuntary responses, which manifest in the form of more news coverage and publications. The evidence presented highlights the strong correlation between news sentiment and market returns, and demonstrates the benefits of advancing knowledge in data-driven modeling and its interaction with market movements.

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Section: News Sentimentmentioning

confidence: 99%

News Sentiment to Market Impact and its Feedback Effect

Liu

Yang

2015

SSRN Journal

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“…Unlike other studies who track the sentiment of terms in comments from users on news site where there is a lot of spam, advertising and hate [76] or select a set of specific keywords for Twitter streams [126,78], our system uses a combination of both approaches. Relying on headlines corpus we assure that the terms extracted are relevant (and may have a public opinion associated) and using tweets, we guarantee that the opinions retrieved are not completely anonymous and, therefore, hate, advertise and insulting comments are less common.…”

Section: Term Sentiment Analysismentioning

confidence: 99%

Lexicon Expansion System for Domain and Time Oriented Sentiment Analysis

Guimarães

Torgo

Figueira

2016

Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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“…Emotions in text may be expressed explicitly (for example, emoticons and lexicon) (Fukushima et al, 2008;Loia and Senatore, 2013;Ptaszynski et al, 2013) as well as implicitly (Balahur et al, 2012;Lau et al, 2014;Wang et al, 2013b). Affective computing enables companies to care more about their customers (Bagheri et al, 2013) and is useful for market prediction (Lassen et al, 2014;Li and Li, 2013;Milea et al, 2012;Nassirtoussi et al, 2014;Zhang et al, 2009), assists in diagnosing patients' suicidal levels (Desmet and Hoste, 2013;Pestian et al, 2010a;2010b) and allows the related parties to gauge public perception towards events (Loia and Senatore, 2013;Moreo et al, 2012). The advancements in affective computing allow applications to sense and deliver services tailored to customer needs, but issues such as privacy need to be observed.…”

Section: Applications Of Sentiment Analysismentioning

confidence: 99%

Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

Sharef¹,

Zin²,

Nadali³

2016

Journal of Computer Science

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Abstract:The ability to exploit public sentiment in social media is increasingly considered as an important tool for market understanding, customer segmentation and stock price prediction for strategic marketing planning and manoeuvring. This evolution of technology adoption is energised by the healthy growth in big data framework, which caused applications based on Sentiment Analysis (SA) in big data to become common for businesses. However, scarce works have studied the gaps of SA application in big data. The contribution of this paper is two-fold: (i) this study reviews the state of the art of SA approaches. including sentiment polarity detection, SA features (explicit and implicit), sentiment classification techniques and applications of SA and (ii) this study reviews the suitability of SA approaches for application in the big data frameworks, as well as highlights the gaps and suggests future works that should be explored. SA studies are predicted to be expanded into approaches that utilise scalability, possess high adaptability for source variation, velocity and veracity to maximise value mining for the benefit of the users.

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Lexicon-based Comments-oriented News Sentiment Analyzer system

Cited by 121 publications

References 38 publications

News Sentiment to Market Impact and its Feedback Effect

News Sentiment to Market Impact and its Feedback Effect

Lexicon Expansion System for Domain and Time Oriented Sentiment Analysis

Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

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