A Sentiment Detection Engine for Internet Stock Message Boards
This is a Paper summary for 10-802 Analysis of Social Media during Fall 2012.
Contents
Citation
Christopher Chua, Maria Milosavljevic, and James R. Curran. 2009. A sentiment detection engine for internet stock message boards. In Proceedings of the Australasian Language Technology Association Workshop 2009.
Online Version
Summary
This article presents a solution for classifying investor sentiment on internet stock message boards and developed on prior work, which deals with messy/sparse data sets. The authors use a variation of Bayes classifier with feature selection and specifically implement Naive Bayes and Support Vector Machines (SVM).
Background
Sentiment prediction has been applied to many discussion boards and forums
Dataset
The data set used for the study was HotCopper, which is the most popular investment forum for the Australian market. The posts include author self-reported sentiment labels, allowing us to best apply sentiment analysis to this corpus.
Methods
The automated sentiment detection engine was implemented using a few different classifiers: Bernoulli Naive Bayes and Complement Naive Bayes. Both models were used with feature selection based on the information gain approach.
