Theresa Wilson and Janyce Wiebe
We present work investigating the topic dependence of words and phrases that have been used in automatic opinion and sentiment recognition. This work is based on machine learning experiments in opinion recognition using topics for cross validation instead of random splits of the data. We find that the clues from previous work are very robust to changes in topic. Surprisingly, while bag-of-words features are not as robust, they do not degrade as much as expected. The best results are obtained when all clues are combined.