Sentiment API Technical Documentation


The aim is to evaluate sentiment polarity (Negative/Positive) at several levels of granularity:

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.

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 an unsupervised approach. We composed a large sentiment dictionary from several open sources, as described below.

Sentiment dictionary

We assembled a sentiment dictionary from three sources:

  1. SentiWordNet: (small)
  2. MPQA opinion corpus: (large)
  3. Stanford IMDB review dataset: (very large)

From each of the above sources we extracted scores in an unique format, namely one score that is between 0 and 1, where the polarity is positive if score is close to 1, and negative, if it is close to 0. It can be expressed also as two scores, positive and negative, that sum up to 1.

SentiWordNet processing:

Terms of SentiWordNet are assigned polarity scores in dependence of their synset. Therefore, one term can occur several times, with different meanings and different polarity scores. We aggregated the scores into one, as follows:

MPQA processing:

The dataset is annotated with positive, negative and neutral, without probabilities. We assigned:
w positive, score_MPQA(w) = 1
w negative, score_MPQA(w) = 0
w neutral, score_MPQA(w) = 0.5

IMDB processing:

We obtain probabilities from counts as follows:

score_IMDB = P(positive|w) = count(w in positive documents) / count(w in documents)

Aggregation into one final score:

score(w) = 0.4 score_SentiWordNet(w) + 0.4 score_MPQA(w) + 0.2 score_IMDB(w)

Sentiment evaluation algorithms

Pipeline for document sentiment:

  1. Tokenization (+ stemming)
  2. Mapping to dictionary
  3. Sentiment evaluation: average over the scores of all mapped words

Pipeline for paragraph:

  1. Tokenization (+ stemming)
  2. Mapping to dictionary
  3. Paragraph identification
  4. Averaging scores of mapped words per paragraph

Pipeline for entity sentiment:

  1. Concept tagging
  2. Tokenization
  3. Segmentation: using parsing, identify which tokens refer directly to the target entity (not available in the current version)
  4. Map tokens to the senti-dictionary
  5. Sentiment evaluation for the target entity: