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Just how Mad are you? Finding Strong and Weak Opinion Clauses

Theresa Wilson, Janyce Wiebe, Rebecca Hwa

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Summary

This paper presents results that delve deeper into the sentiment analysis task in two respects:

• First, it explores sentiment analysis at the sub-sentence level, specifically at the level of each individual clause in a sentence
• Second, it deals with the task of predicting strength of opinions, rather than simple yay/nay judgments
  • Strength is annotated at 4 levels - high, medium, low and neutral, where neutral is absence of opinion

The dataset used is the Multi-perspective Question Answering (MPQA) corpus of opinion annotations, where all the expressions in context were judged and marked up by the annotators, thus producing fine-grained sentiment markup.

The key contribution of the paper is in the form of strength-based grouping of syntactic and other subjectivity features that boost the accuracy significantly over the conventional features in sentiment analysis. Syntactic features are combined with a variety of previously used subjectivity clues in the form of manually developed lists or bootstrapped list of subjectivity features.

The syntactic features are initially extracted in a dependency parse representation, which is essentially a set of triples, where each triple contains a head word (usually with its Part-of-Speech tag), its modifier word (with its Part-of-Speech tag) and the relationship between them. Using the dependency parses, five classes of syntactic features are formed for each word in the parse:

1. root(w, t): whether word w with POS tag t is the root of the dependency tree (essentially the main verb)
2. leaf(w, t): whether word w with POS tag t is a leaf in the dependency tree, and therefore has no modifiers
3. node(w, t): whether word w with POS tag t is some node in the dependency tree (essentially the word-POS tag pair forms a feature)
4. bilex(w, t, r, w_c, t_c): whether word w with POS tag t is modified by word w_c with POS tag t_c with the relationship between them being r
5. allkids(w, t, r₁, w₁, t₁, ..., r_n, w_n, t_n): whether word w with POS tag t has n children with tags t₁ through t_n and relationships r₁ through r_n.

These features or clues are evaluated for their usefulness using a development set and only a certain subset is selected.

The features used are further filtered and organized into sets based on how indicative they are of a particular strength level in a sentiment. This strength-based filtering and organization of features yielded the best results in all the experiments presented in the paper.

The paper contains detailed results about classification at different levels - beginning at sentence level and going up to deeply nested clause level; and also for the different sets of features and classifiers used.

Overall, this is a careful and thorough analysis of the sentiment strength classification task at the clause level.