@inproceedings{tabari-etal-2017-sentiheros,
title = "{S}enti{H}eros at {S}em{E}val-2017 Task 5: An application of Sentiment Analysis on Financial Tweets",
author = "Tabari, Narges and
Seyeditabari, Armin and
Zadrozny, Wlodek",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2146",
doi = "10.18653/v1/S17-2146",
pages = "857--860",
abstract = "Sentiment analysis is the process of identifying the opinion expressed in text. Recently it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. SemEval-2017 task 5 focuses on the financial market as the domain for sentiment analysis of text; specifically, task 5, subtask 1 focuses on financial tweets about stock symbols. In this paper, we describe a machine learning classifier for binary classification of financial tweets. We used natural language processing techniques and the random forest algorithm to train our model, and tuned it for the training dataset of Task 5, subtask 1. Our system achieves the 7th rank on the leaderboard of the task.",
}
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%0 Conference Proceedings
%T SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets
%A Tabari, Narges
%A Seyeditabari, Armin
%A Zadrozny, Wlodek
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F tabari-etal-2017-sentiheros
%X Sentiment analysis is the process of identifying the opinion expressed in text. Recently it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. SemEval-2017 task 5 focuses on the financial market as the domain for sentiment analysis of text; specifically, task 5, subtask 1 focuses on financial tweets about stock symbols. In this paper, we describe a machine learning classifier for binary classification of financial tweets. We used natural language processing techniques and the random forest algorithm to train our model, and tuned it for the training dataset of Task 5, subtask 1. Our system achieves the 7th rank on the leaderboard of the task.
%R 10.18653/v1/S17-2146
%U https://aclanthology.org/S17-2146
%U https://doi.org/10.18653/v1/S17-2146
%P 857-860
Markdown (Informal)
[SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets](https://aclanthology.org/S17-2146) (Tabari et al., SemEval 2017)
ACL