27.3294, FYI: SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

Tue Aug 16 17:52:32 UTC 2016

LINGUIST List: Vol-27-3294. Tue Aug 16 2016. ISSN: 1069 - 4875.

Subject: 27.3294, FYI: SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

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Date: Tue, 16 Aug 2016 13:52:10
From: Manel Zarrouk [manel.zarrouk at insight-centre.org]
Subject: SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

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

Website: http://alt.qcri.org/semeval2017/task5/

Sentiments and opinions expressed on social media and news can strongly affect
market dynamics. Sentiment analysis over the financial domain depends on
particular computational linguistics challenges such as the modelling of
finance-specific lexicon, interpretation of economic and financial events, use
of financial background knowledge, on the top of usual challenges for
sentiment analysis such as sarcasm/irony detection, lack of context and poorly
structured language. 

Research effort is required to overcome and address these issues. This Semeval
task aims at catalyzing discussions around approaches of semantic
interpretation of financial texts by targeting a financial sentiment analysis
task, which identifies bullish (optimistic; believing that the stock price
will increase) and bearish (pessimistic; believing that the stock price will
decline) sentiment associated with companies and stocks.

The Task:

Participating systems will need to address the following task: given a text
instance (microblog message in Track 1, news statement or headline in Track
2), predict the sentiment score for each of the companies/stocks mentioned.
Sentiment values need to be floating point values in the range of -1 (very
negative/bearish) to 1 (very positive/bullish), with 0 designating neutral
sentiment. 

Track 1 - Microblog Messages:

- StockTwits Messages: Consists of microblog messages focusing on stock market
events and assessments from investors and traders, exchanged via the
StockTwits microblogging platform. Typical stocktwits consist of references to
company stock symbols (so-called cashtags - a stock symbol preceded by “$”,
e.g. “$AAPL” for  the company Apple Inc.), a short supporting text and
references links.

- Twitter Messages: Twitter posts containing company stock symbols (cashtags),
in the same style as the StockTwits messages.

Example: 'Este Lauder beats on Revenues and EPS and boosts dividend 25% -
global growth in the Middle Class trend continues. $EL $NKE $SBUX $AAPL'

Track 2 - News:

News headlines from financial sources such as Bloomberg, Reuters, Financial
Times, Wall Street Journal.

Example: 'First Solar, Vivint Solar Lead Short Interest Trend'

The test collection contains the companies/cashtags which are referenced in
the text, the text spans associated to each company (for microblogs) and the
associated sentiment scores.

Important Dates:

01 Aug 2016: Trial data ready (see
http://alt.qcri.org/semeval2017/task5/index.php?id=data-and-tools)
05 Sep 2016: Training data ready
09 Jan 2017: Evaluation starts
30 Jan 2017: Evaluation ends
06 Feb 2017: Results posted
27 Feb 2017: Paper submissions due
03 Apr 2017: Author notifications
17 Apr 2017: Camera ready submissions due

Questions and comments: semeval-2017-task-5 at googlegroups.com

Registration for the task: http://bit.ly/2b9fb7o

Dr. Manel Zarrouk
Postdoctoral Researcher & Adjunct Lecturer
Knowledge Discovery Unit,
INSIGHT @ NUI Galway
manel.zarrouk at insight-centre.org
manel at mzarrouk.net
http://mzarrouk.net

Linguistic Field(s): Computational Linguistics

Subject Language(s): English (eng)

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