This paper is published in Volume-7, Issue-4, 2021
Area
Machine Learning
Author
Tavva Sai Prathyusha, Thummapala Mounika, Vemaraju Siri chandana, Vysyaraju priyanka, Seepana Priyanka
Org/Univ
Andhra University College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
Pub. Date
14 July, 2021
Paper ID
V7I4-1349
Publisher
Keywords
Sentiment analysis, TF-IDF, Bag-of-words, Random forest, Multinomial Naïve Bayes, Passive-Aggressive Classifier

Citationsacebook

IEEE
Tavva Sai Prathyusha, Thummapala Mounika, Vemaraju Siri chandana, Vysyaraju priyanka, Seepana Priyanka. Stock Sentiment Analysis using News Headlines, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Tavva Sai Prathyusha, Thummapala Mounika, Vemaraju Siri chandana, Vysyaraju priyanka, Seepana Priyanka (2021). Stock Sentiment Analysis using News Headlines. International Journal of Advance Research, Ideas and Innovations in Technology, 7(4) www.IJARIIT.com.

MLA
Tavva Sai Prathyusha, Thummapala Mounika, Vemaraju Siri chandana, Vysyaraju priyanka, Seepana Priyanka. "Stock Sentiment Analysis using News Headlines." International Journal of Advance Research, Ideas and Innovations in Technology 7.4 (2021). www.IJARIIT.com.

Abstract

Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such as financial news articles about a company and predicting its future stock trend with news sentiment classification. Assuming that news articles have impact on stock market, this is an attempt to study relation between news and stock trend. To show this, we created three different classification models which depict polarity of news articles being positive or negative.