| First, let me say that this is really creative work and I'm glad it's being presented at EMNLP. "Sentiment analysis" is too broad of a category to really cover in a single article like this. What they've done is taken a very difficult problem, sentence-level binary sentiment, and made solid progress on it. The baseline for this dataset using totally naive techniques is around 75%, and their results are the state of the art. The move from 85% to 95% isn't really an interesting one. What really matters is exploring the numerous other open questions in the field of affect recognition, notably two thing: * Sentiment at different granularities. Document level analysis has been far above 90% for years; this work is pushing forward sentence level. Other work is making great progress on targeted opinions even finer-grained than that, like looking at specific attributes of products. What if you like a movie's acting but not its plot? This structured nuance is not addressed here. * Domain adaptation. You talk about movies in a different way from almost anything else. A movie review is positive if it's unpredictable; your opinion of the unpredictability of dishwashers or political candidates is probably different. For anything beyond movie reviews this method may work, but this particular dataset certainly won't. Looking forward to seeing more from this group, as ever; Chris Manning's research team has an excellent reputation in the field. |