SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial
            Microblogs and News

Cortis, Keith; Freitas, André; Daudert, Tobias; Huerlimann, Manuela; Zarrouk, Manel; Handschuh, Siegfried; Davis, Brian

doi:10.18653/v1/s17-2089

Cited by 137 publications

(112 citation statements)

References 40 publications

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“…Web-derived text seems to thrive as a source of AM pipelines, including the task of relation identification, but the source of data usually is a more structured source of text, such as debate forums [86,87,88,89]. Although the information found in social media are characterized as noisy text and it is far from an ideal scenario for AM, the constant generation of content allows to the researchers to conduct research including the time axis in order to understand users' behaviour [72,90] and evaluate their impact beyond the network [91,92]. Users in social media platforms usually express emotions or quick messages with very little argumentation, however the introduction of argumentative features can enhance other NLP tasks [60,66].…”

Section: Relations Identificationmentioning

confidence: 99%

The evolution of argumentation mining: From models to social media and emerging tools

Lytos

Λάγκας²,

Sarigiannidis

et al. 2019

Information Processing & Management

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Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.

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Section: Relations Identificationmentioning

confidence: 99%

The evolution of argumentation mining: From models to social media and emerging tools

Lytos

Λάγκας²,

Sarigiannidis

et al. 2019

Information Processing & Management

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“…The SemEval-2017 Task 5 is divided into two tracks which each consider a different data basis (Cortis et al, 2017). The objective of both tracks is the prediction of a sentiment score with reference to a company or a stock in a given piece of text.…”

Section: Task Descriptionmentioning

confidence: 99%

HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods

Cabanski¹,

Romberg²,

Conrad

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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In this Paper a system for solving SemEval-2017 Task 5 is presented. This task is divided into two tracks where the sentiment of microblog messages and news headlines has to be predicted. Since two submissions were allowed, two different machine learning methods were developed to solve this task, a support vector machine approach and a recurrent neural network approach. To feed in data for these approaches, different feature extraction methods are used, mainly word representations and lexica. The best submissions for both tracks are provided by the recurrent neural network which achieves a score of 0.729 in track 1 and 0.702 in track 2.

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“…The decision in favor of PyTorch is made due to my experience in Python and its support by Facebook, Twitter, and NVIDIA. Initially, publicly available Twitter gold standards (GS) such as the Semeval 2017 Task 5 one (Cortis et al, 2017) comprising 2494 Tweets will be used for training and testing. This GS will be replaced by a newly created, more problem-specific gold standard only focusing on entities tracked on Twitter.…”

Section: Microblog Analysermentioning

confidence: 99%

Analysing Market Sentiments: Utilising Deep Learning to Exploit Relationships within the Economy

Daudert

2017

RANLP - Student Research Workshop

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In today's world, globalisation is not only affecting inter-culturalism but also linking markets across the globe. Given that all markets are affecting each other and are not only driven by fundamental data but also by sentiments, sentiment analysis regarding the markets becomes a tool to predict, anticipate, and milden future economic crises such as the one we faced in 2008. In this paper, an approach to improve sentiment analysis by exploiting relationships among different kinds of sentiment, together with supplementary information, from and across various data sources is proposed.

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SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

Cited by 137 publications

References 40 publications

The evolution of argumentation mining: From models to social media and emerging tools

The evolution of argumentation mining: From models to social media and emerging tools

HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods

Analysing Market Sentiments: Utilising Deep Learning to Exploit Relationships within the Economy

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