Armin Seyeditabari


2018

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Causality Analysis of Twitter Sentiments and Stock Market Returns
Narges Tabari | Piyusha Biswas | Bhanu Praneeth | Armin Seyeditabari | Mirsad Hadzikadic | Wlodek Zadrozny
Proceedings of the First Workshop on Economics and Natural Language Processing

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. In this paper, we use a public dataset of labeled tweets that has been labeled by Amazon Mechanical Turk and then we propose a baseline classification model. Then, by using Granger causality of both sentiment datasets with the different stocks, we shows that there is causality between social media and stock market returns (in both directions) for many stocks. Finally, We evaluate this causality analysis by showing that in the event of a specific news on certain dates, there are evidences of trending the same news on Twitter for that stock.

2017

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SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets
Narges Tabari | Armin Seyeditabari | Wlodek Zadrozny
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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.