The aim is to evaluate sentiment polarity (Negative/Positive) at several levels of granularity:
- document (overall sentiment): appropriate for blog posts or technical review articles, estimates whether the author's opinion on the topic is generally positive or negative. Strong polarity means the author is very subjective.
- paragraph (aspect oriented): each paragraph expresses an aspect of the overall topic discussed in the document. News articles that are bound to present a balanced view on an event, are expected to alternate positive and negative aspects in the constituent paragraphs.
- entity (very specific target): appropriate for extracting detailed opinion on products and components, events, and others, together with aggregation over a corpus, for market analysis.
Sentiment prediction can be supervised, semi-supervised or unsupervised.
Supervised approaches rely on annotated datasets. Given the strong domain specificity, it is important that a large corpus from the target domain is available. When not available, domain adaptation methods can be used, that rely on a large out-of-domain corpus and a small supplementary target-domain annotated corpus.
Unsupervised methods rely on sentiment dictionaries: large lists of words with scores quantifying their polarity. Mapping to dictionary and aggregation statistics are used to evaluate sentiment in free text.
Semi-supervised approaches rely on a small set of annotated texts or small polarity dictionaries, that are expanded by either bootstrap methods, or by using external knowledge-bases like Wordnet.
Input: a sentiment dictionary of sentiwords, i.e. words that have some polarity, together with scores that quantify how positive or how negative they are.
We assembled a sentiment dictionary from three sources:
- Stanford IMDB
Pipeline for document sentiment:
- Sentiment mapping
Pipeline for entity sentiment:
- Concept tagging
- Sentiment mapping
- Sentiment evaluation