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

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.

Causality Analysis of Twitter Sentiments and Stock Market Returns

“…We used Amazon Mechanical Turk to manually label our stock market tweets. In order to create a classification model, so it can be used to predict more tweets in the future analysis, we applied the same preprocessing technique and classification models explained in detail by Tabari et. al Tabari et al (2017).…”

“…In order to create a classification model, so it can be used to predict more tweets in the future analysis, we applied the same preprocessing technique and classification models explained in detail by Tabari et. al Tabari et al (2017). In preprocessing phase, after tokenization, all numbers were substituted with <num> tag.…”

SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection

“…There are several machine learning techniques that can be used for sentiment intensity prediction or emotion intensity prediction. Some of the approaches inlclude Artificial Neural Network (ANN) (Sudipta et al, 2017), Random Forests, Support Vector Machine (SVM), Naive Bayes (NB) (Tabari et al, 2017), Multi-Kernel Gaussian Process (MKGP) (Angel Deborah et al, 2017a,b), AdaBoost Regressor (ABR), Bagging Regressor (BR) (Jiang et al, 2017) and Deep Learning (DL) (Pivovarova et al, 2017). function (in the input layer the activation function is not applied).…”