FSAL: A Tailor-Made Financial Sentiment Lexicon in Spanish for the Argentinian Markets (BYMA)

<p class="p1"<During the last decade studies have shown that lexicon-based Sentiment Analysis of tweets combined with Machine Learning techniques can be used to enhance Algorithmic Trading strategies. The aim of the present work is to show how a specific domain lexicon in finance for...
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Autor*in:	Juan Pablo Braña [verfasserIn] Alejandra M. J. Litterio [verfasserIn] Alejandro Fernández [verfasserIn]

Format:	E-Artikel
Sprache:	Spanisch

Erschienen:	2018

Schlagwörter:	análisis de sentimiento lexicón financiero trading algorítmico machine learning

Übergeordnetes Werk:	In: Revista Abierta de Informática Aplicada - Universidad Abierta Interamericana, 2019, 2(2018), 1, Seite 5-22
Übergeordnetes Werk:	volume:2 ; year:2018 ; number:1 ; pages:5-22

Links:	Link aufrufen Link aufrufen Journal toc

Katalog-ID:	DOAJ027860973

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title_auth	FSAL: A Tailor-Made Financial Sentiment Lexicon in Spanish for the Argentinian Markets (BYMA)
abstract	<p class="p1"<During the last decade studies have shown that lexicon-based Sentiment Analysis of tweets combined with Machine Learning techniques can be used to enhance Algorithmic Trading strategies. The aim of the present work is to show how a specific domain lexicon in finance for the Argentinian Markets (FSAL) provides a better outcome than a generic lexicon (SDAL). First, we introduce a finance tailor-made lexicon. Secondly, we experimentally show that our lexicon outperforms a general purpose one on a corpus of tweets previously classified collaboratively by<span class="Apple-converted-space"< </span<specialists in finance. Then, we compare the lexicons applying three different Machine Learning algorithms. Finally, we introduce some preliminary results and conclusions.</p<
abstractGer	<p class="p1"<During the last decade studies have shown that lexicon-based Sentiment Analysis of tweets combined with Machine Learning techniques can be used to enhance Algorithmic Trading strategies. The aim of the present work is to show how a specific domain lexicon in finance for the Argentinian Markets (FSAL) provides a better outcome than a generic lexicon (SDAL). First, we introduce a finance tailor-made lexicon. Secondly, we experimentally show that our lexicon outperforms a general purpose one on a corpus of tweets previously classified collaboratively by<span class="Apple-converted-space"< </span<specialists in finance. Then, we compare the lexicons applying three different Machine Learning algorithms. Finally, we introduce some preliminary results and conclusions.</p<
abstract_unstemmed	<p class="p1"<During the last decade studies have shown that lexicon-based Sentiment Analysis of tweets combined with Machine Learning techniques can be used to enhance Algorithmic Trading strategies. The aim of the present work is to show how a specific domain lexicon in finance for the Argentinian Markets (FSAL) provides a better outcome than a generic lexicon (SDAL). First, we introduce a finance tailor-made lexicon. Secondly, we experimentally show that our lexicon outperforms a general purpose one on a corpus of tweets previously classified collaboratively by<span class="Apple-converted-space"< </span<specialists in finance. Then, we compare the lexicons applying three different Machine Learning algorithms. Finally, we introduce some preliminary results and conclusions.</p<
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score	7.3982964

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FSAL: A Tailor-Made Financial Sentiment Lexicon in Spanish for the Argentinian Markets (BYMA)

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