Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification Aaron Michelony CMPS245 April 12, 2011

Overview Goal: Improve opinion polarity classification with discourse relations Use the AMI meeting corpus Set baselines Use supervised local classifier Use ICA algorithm and integer linear programming (ILP) algorithm Combine the ICA algorithm and ILP algorithm

Sample Discourse DA-1:... this kind of rubbery material, DA-2: it's a bit more bouncy, DA-3: like you said they get chucked around a lot, DA-4: A bit more durable and that can also be ergonomic and DA-5: it kind of feels a bit different from all the other remote controls. Explicit targets are in italics, individual opinion expressions are shown in bold.

Class distributions Connected: instances that are related via discourse relations Singletons: instances not related via discourse relations 7 meetings, 4606 DA instances, 1935 (42%) have opinion annotations

Base and Base-2 Base classifies the test data based on the overall distribution of the classes in the training data. Base-2 constructs separate distributions for connected instances and singletons.

Local Classifier Supervised classifier using SVM. Uses polarity lexicons, DA tags and unigrams Used by ICA algorithm and ILP.

ICA Algorithm Uses two classifiers: a local classifier and a relational classifier. The relational classifier is also an SVM. Two main phases: bootstrapping and iterative phases. Bootstrapping phase: Initialize the polarity of each instance to the most likely value given only the local classifier and its features Iterative phase: Create random ordering of all the instances and apply the relational classifier to each instance using the relational features. Repeat until a stopping criterion is met (30 iterations).

Relational Features 59 relational features All combinations of a, t, f, t', f' a = {positive or negative, positive, negative} t = {same, alt} f = {reinforcing, non-reinforcing} t' = {same or alt, same, alt} f' = {reinforcing or non-reinforcing, reinforcing, non- reinforcing} Ex: Percent of neighbors with polarity type positive, that are related via a reinforcing frame relation.

Integer Linear Programming i represents a DA instance in the dataset -1 * sum( p_i*x_i + q_i*y_i + r_i*z_i ) + sum( epsilon_ij ) + sum( delta_ij ) x_i + y_i + z_i = 1 x, y and z are binary class variables corresponding to positive, negative and neutral, respectively epsilon and delta are binary slack variables that correspond to discourse constraints. e_ij is the equal-polarity constraint o_ij is the opposite-polarity constraint

Integer Linear Programming 2 |x_i - x_j| <= 1 - e_ij + epsilon_ij, forall i != j |y_i - y_j| <= 1 - e_ij + epsilon_ij, forall i != j Via these equations, we ensure that instances i and j don't have opposite polarity when e_ij = 1. -(x_i + y_i) <= -l_i forall i l_i = 1 if the instance i participates in one or more discourse relation Guides the convergence to non-neutral category |x_i + x_j - 1| <= 1 - o_ij + delta_ij, forall i != j |y_i + y_j - 1| <= 1 - o_ij + delta_ij, forall i != j When o_ij = 1, x_i and x_j take on opposite values. When o_ij = 0, the variables are independent and the constraints are relaxed when delta_ij = 1.

Evaluation ILP performs better than ICA on connected instances, while ICA performs better on singletons. HYB is hybrid classifier.

Precision, Recall, Fmeasure

The End