A feature dependent method for opinion mining and classification

  • Balahur A
  • Montoyo A
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Abstract

Mining the web for customer opinion on different products is both a
useful, as well as challenging task. Previous approaches to customer
review classification included document level, sentence and clause level
sentiment analysis and feature based opinion summarization. In this
paper, we present a feature driven opinion summarization method, where
the term ``driven{''} is employed to describe the concept-to-detail
(product class to product-specific characteristics) approach we took.
For each product class we first automatically extract general features
(characteristics describing any product, such as price, size, design),
for each product we then extract specific features (as picture
resolution in the case of a digital camera) and feature attributes
(adjectives grading the characteristics, as for example high or low for
price, small or big for size and modern or faddy for design). Further
on, we assign a polarity (positive or negative) to each of the feature
attributes using a previously annotated corpus and Support Vector
Machines Sequential Minimal Optimization{[}1] machine learning with the
Normalized Google Distance{[}2]. We show how the method presented is
employed to build a feature-driven opinion summarization system that is
presently working in English and Spanish. In order to detect the product
category, we use a modified system for person names classification. The
raw review text is split into sentences and depending on the product
class detected, only the phrases containing the specific product
features are selected for further processing. The phrases extracted
undergo a process of anaphora resolution, Named Entity Recognition and
syntactic parsing. Applying syntactic dependency and part of speech
patterns, we extract pairs containing the feature and the polarity of
the feature attribute the customer associates to the feature in the
review. Eventually, we statistically summarize the polarity of the
opinions different customers expressed about the product on the web as
percentages of positive and negative opinions about each of the product
features. We show the results and improvements over baseline, together
with a discussion on the strong and weak points of the method and the
directions for future work.

Author-supplied keywords

  • Normalized google distance
  • Opinion mining
  • SVM machine learning
  • Summarization

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Authors

  • Alexandra Balahur

  • Andrés Montoyo

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