Sentiment Composition of Words with Opposing Polarities
This addresses sentiment analysis challenges for natural language processing, but it is incremental as it focuses on a specific linguistic phenomenon.
The paper tackled the problem of sentiment composition in phrases with both positive and negative words by compiling and annotating a dataset, and their best system achieved over 80% accuracy in predicting sentiment scores.
In this paper, we explore sentiment composition in phrases that have at least one positive and at least one negative word---phrases like 'happy accident' and 'best winter break'. We compiled a dataset of such opposing polarity phrases and manually annotated them with real-valued scores of sentiment association. Using this dataset, we analyze the linguistic patterns present in opposing polarity phrases. Finally, we apply several unsupervised and supervised techniques of sentiment composition to determine their efficacy on this dataset. Our best system, which incorporates information from the phrase's constituents, their parts of speech, their sentiment association scores, and their embedding vectors, obtains an accuracy of over 80% on the opposing polarity phrases.