Analyzing Stock Market Movements Using Twitter Sentiment Analysis

By Tushar Rao, Saket Srivastava. 2012

163 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pushpak Bhattacharyya
2. Asif Ekbal
3. Abhishek Kumar
4. Deepanway Ghosal
5. Ashima Yadav
6. Md. Shad Akhtar
7. Dinesh Kumar Vishwakarma

In Management Science and Operations Research

1. Pushpak Bhattacharyya
2. Deepanway Ghosal

Citations in the corpus

• Sentiment Predictability for Stocks (2017, neutral): “s work, there were numerous works studying which individual stocks from the S+P 500 indices and the Dow Jones Averages, followed the Twitter sentiment prediction more closely. These include [RACM15], [RS12] However, this is perhaps highly similar to finding which stocks have high correlation to those”
• A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis (2017, background): “If the agreement score is 1 then it implies that the instance is of having either high positive or negative sentiment whereas, a 0 agreement score indicates a mixed or disharmony in the positive and negative sentiment implying the sentence is not polar (Rao and Srivastava, 2012).”
• Sentiment analysis using deep learning architectures: a review (2019, background): “Rao and Srivastava (2012) analyzed the relationship between the sentiment of tweets from DJIA (Dow Jones Industrial Average), NASDAQ-100, and 13 other companies and its impact onmarket performance.”