SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets

Narges Tabari, Armin Seyeditabari, Wlodek Zadrozny


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.
Anthology ID:
S17-2146
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
857–860
Language:
URL:
https://www.aclweb.org/anthology/S17-2146
DOI:
10.18653/v1/S17-2146
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2146.pdf