Jiménez-Zafra, S. M., Martín-Valdivia, M. T., Martínez-Cámara, E., & Ureña-López, L. A. (2016). Combining resources to improve unsupervised sentiment analysis at aspect-level. Journal of Information Science, 42(2), 213–229. https://doi.org/10.1177/0165551515593686
This paper presents an approach for unsupervised aspect-based sentiment analysis that uses Freebase for extracting relevant aspects from restaurant and laptop reviews.
- Freebase contains different domains such as music, computers, food, etc. Querying such a domain yields properties and instances which are considered relevant aspects of that particular domain.
- The sentiment analysis uses dependency parsing to obtain relations between aspects and sentiment terms. Negation is implemented based on identifying negation triggers such as "not", "n't" "no" and "never" within a fixed window.
- The sentiment analysis draws upon a voting system based on (i) the Bing Liu Lexicon, (ii) MPQA and (iii) SentiWordNet. An aspect is considered positive/negative, if at least two classifiers agree on that particular sentiment.
- Domain experts manually assign aspects to categories (e.g. dishes to the category food) to determine the category sentiment.