This document summarizes a research paper that proposes a novel unsupervised approach to identify evaluative sentences in online discussions. The approach extracts aspects and expands evaluation and emotion lexicons in an unsupervised manner. It then models the interactions between aspects, evaluation words, and emotion words to classify a sentence as either evaluative or non-evaluative. The classification is done in two steps - first by calculating an evaluative score for aspects, and then comparing the sums of matched evaluation and emotion words. The approach is empirically evaluated and its parameters are analyzed.

1. Identifying Evaluative Sentences in Online
Discussions
Zhongwu Zhai Bing Liu Lei Zhang Hua Xu Peifa Jia
Tsinghua National Lab for Info. Sci. and Tech
University of Illinois at Chicago
Ιωάννης Στάης

2. Opinion Mining Research
Current research has been focused on extracting opinions from
product reviews (opinion rich and without irrelevant information)
More important than mining reviews, because discussions often
focus on current events and issues, and the latest products
Main Problem to deal: the participants can interact with each
other. Discussions can get emotionally charged and off topic
Problem Description: Clearly, our problem is a classification
problem with 2 classes, evaluative and non-evaluative.

3. Proposed Solution:
Novel Unsupervised Approach
Input: a set of evaluative opinion words (beautiful, expensive, ugly),a set
of emotion words (sad, surprise, anger)
Observations:
▫ An evaluative opinion should comment on a topic or some aspects
of a topic:
"The German team was strong”
▫ Evaluation words and emotion words are indications of evaluative
and emotional sentences, respectively
"German team was weak today“
"I felt sad for the German team“
Target : Extract aspect and classify
▫ In each domain some aspects can be associated with both evaluative
and emotions opinions
▫ The original lists of evaluation words and emotion words can have
errors because the same words may take on different meanings in
different domains.
Additional targets : Expand lists of words and exploit the inter-relationships

4. Proposed Technique

5. Extraction of aspects & expansion of evaluation and emotion
lexicons
Extract aspects using evaluation or emotion words: (E to A) Noun term
near a given or extracted evaluation or emotion word E
(no adjective or noun terms between N and E). (the nearest noun term is selected)
Argentina defense is very weak
Extract aspects using extracted aspects: (A to A)*If one of the conjoined
noun terms is an extracted aspect, then the other noun term is also an aspect
Löw and the players are both hard-working
If a noun term N appears before or after an extracted aspect A and they are
separated by no letter, then N is extracted as an aspect.
Argentina defense is very weak
Extract evaluation words and emotion words using the given or extracted
evaluation words and emotion wordsrespectively.(E to E)*If an adjective
appears within a text window of three words (before or after) an evaluation
word E, then it is a new evaluation word.
The German defense is proactive and strong

6. Interaction modeling of aspects, evaluation words
and emotion words
• Aspect with many evaluation words: probably an
evaluative sentence (give high score).
• Aspect with many emotion words: probably not an
evaluative sentence (give low score).
• An evaluation word that does not modify high
scored aspects: probably a wrong evaluation word
(give low score)
• The more evaluative the aspects are, the less
emotional their associated emotion words should be

7. Interaction modeling of aspects, evaluation words
and emotion words

8. Classification
• Step 1:
▫ Find the highest evaluative score (topA) of an aspect in a
sentence
▫ If topA is greater than a pre-defined threshold (the default is
0.6), proceed to step 2
• Step 2:
▫ Match all evaluation and emotion words
▫ If vaSum is greater than moSum, sentence s is classified as
evaluative

9. Empirical Evaluation

10. Influence of the parameters

11. Εσταριστώ