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h2. Introduction

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.

h2. Our approach (for English)

Our tagging services in intended for generic documents, without specified domain (at least in the early stages). Therefore we opted for a

h2. Sentiment dictionary
We assembled a sentiment dictionary from three sources:
* SentiWordnet
* Stanford IMDB

h2. Sentiment evaluation algorithms

Pipeline for document sentiment:
# Sentiment mapping

Pipeline for entity sentiment:
# Concept tagging
# Segmentation
# Sentiment mapping
# Sentiment evaluation