Sentiment Polarity Identification in Financial News: A Cohesion-based Approach

By Ann Devitt, Khurshid Ahmad. Annual Meeting of the Association for Computational Linguistics, 2007

341 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/84799363f756eca10225c103a5cc735e14e238e9

How researchers see this paper

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

1. Xiaodong Li
2. Vadlamani Ravi
3. B. Shravan Kumar
4. Haoran Xie
5. Xiaotie Deng
6. Qing Li
7. Hsinchun Chen
8. Mike Thelwall

Citations in the corpus

• The Operational Value of Social Media Information (2018, background): “Note that simpler approaches to sentiment analysis — such as bag-of-words classifiers, widely adopted in finance, accounting, and computer science (Pang and Lee 2008 and Devitt and Ahmad 2007) — cannot classify our social media information to a satisfactory precision.”
• A survey of the applications of text mining in financial domain (2016, background): “Devitt and Ahmad (2007) proposed a model based on lexical cohesion for finding the sentiment polarity in the financial news.”
• The Stock Sonar - Sentiment Analysis of Stocks Based on a Hybrid Approach (2011, background): “…of financial news, blogs and stock message boards, as well as on stock prediction from these sources, either via sentiment classification or directly (Lavrenko et al. 2000; Das and Chen 2001; Koppel and Shtrimberg 2004; Devitt and Ahmad 2007; O'Hare et al. 2009; Schumaker and Chen 2010).”
• Co-Training for Cross-Lingual Sentiment Classification (2009, background): “Devitt and Ahmad (2007) explore a computable metric of positive or negative polarity in financial news text.”
• A Study of Information Retrieval Weighting Schemes for Sentiment Analysis (2010, background): “Most of the work has focused on analyzing the content of movie or general product reviews, but there are also applications to other domains such as debates (Thomas et al., 2006; Lin et al., 2006), news (Devitt and Ahmad, 2007) and blogs (Ounis et al., 2008; Mishne, 2005).”
• Survey on mining subjective data on the web (2011, methodology): “For example, Devitt and Ahmad (2007) identify sentiment-bearing words in a document by using SentiWord- Net, but then use just their frequencies of occurrence for the classification task.”
• Evaluating sentiment in financial news articles (2012, methodology): “judgment (Devitt and Ahmad, 2007) and has an impact on firm performance (Davis, Piger et al.”
• Identifying and Following Expert Investors in Stock Microblogs (2011, background): “Some of these works focus on document-level sentiment classification (Devitt and Ahmad, 2007; O’Hare et al., 2009).”
• Using SentiWordNet for multilingual sentiment analysis (2008, background): “Ahmad et al. only detect sentiment bearing phrases.”
• News impact on stock price return via sentiment analysis (2014, background): “Devitt and Ahmad [11] propose a cohesion measure based on graph-based text representation to compute the polarity of financial news texts and their impact on financial markets.”
• Semantic Frames to Predict Stock Price Movement (2013, background): “Recent work has applied NLP techniques to various financial media (conventional news, tweets) to detect sentiment in conventional news (Devitt and Ahmad, 2007; Haider and Mehrotra, 2011) or message boards (Chua et al.”