A cross-lingual sentiment topic model evolution over time

Sentiment analysis in various languages has been a hot research topic with several applications. Most of the existing models have been reported to work well with widely used language. Were the lass directly applying these models to poor-quality corpora often leads to low results. Thus, to deal with these shortcoming we propose a cross-lingual sentiment topic model evolution over time (CLSTOT) which jointly models time with topic and sentiment. In CLSTOT, we consider the mapping between sentiment-aware topics under different cultures and analyze their evolution over time. The topic-specific sentiment is extracted using the entire data and not for each single document. As long as providing sentiment-topic, we can predict the timestamps for each test document by finding its most likely location over the timeline. This is achieved by using inference algorithm which is based on Gibbs Sampling. The experimental results on Chinese and English newsreader dataset; Chinese from SinaNews2, and English from Yahoo1, show that CLSTOT achieves significant improvement over the state-of-the-art.