Riding the tide of sentiment change: sentiment analysis with evolving online reviews

1. Riding the tide of sentiment change: sentiment analysis with evolving online reviews Yang Liu, Xiaohui Yu, Aijun An, Xiangji Huang

2. General Idea • Perform sentiment analysis on IMDB movie review • Using the sentiment to predict box office revenues

3. Sentiment Analysis: PLSA • PLSA can be used as an unsupervised classifier to determine the mixture of sentiment in a review • Incapacity of adapting itself as new data become available • Brutal way is to train the whole model again • This paper proposed two ways to deal with new data

4. Data structure • Only appraisals in reviews are recognized and frequency of appraisals are counted in each reviews • Appraisal lexicon • Polarity means whether there is negation like “not”, “no”, “non’, etc

5. EM algorithm for PLSA

6. Adaptive to new data • Light-weight incremental model • For data do not change dramatically throughout the time

7. Adaptive to new data • Quasi-Bayesian model • X is the available data till time n • Assumed probability distribution on each time interval determined by some parameters • Dirichlet distribution is used φ = {α, β} are the hyperparameters of the Dirichlet distribution

9. Application to sales prediction • Linear regression model

10. Evaluation • 28,353 reviews for 20 drama films released in the US from 1 May 2006 to 1 September 2006 • Box office revenue of the corresponding drama films from 1 May 2006 to 1 September 2006 • 4 time intervals for the training of PLSA • Mean absolute percentage error is used to measure the prediction accuracy

11. Evaluation • Time interval set to be days = 2,4,6,8

12. Evaluation • Time interval set to be day = 5, 10, 15, 20