This document presents an approach to opinion mining that uses a holistic lexicon-based method. It focuses on determining whether opinions on identified features are positive, negative, or neutral. It proposes using an opinion lexicon and handling context-dependent opinion words and implicit features indicated by adjectives. Rules are also introduced to determine opinion polarity across sentences. An evaluation shows this approach achieves precision, recall, and F-score of 0.92, 0.91, and 0.91 respectively.
Part 1 of a 2-day workshop to introduce style in technical communication. Presented to beginning students of technical communication on December 1, 2009 at Kyung Hee University in Suwon, South Korea.
Ontology based opinion mining for book reviewsfirzhan naqash
The recent burst in web usage has contributed to the growth of a number of various online reviews
like consumer product reviews, legal reviews, political reviews, movie reviews and book reviews.
Out of these reviews, some are context sensitive and others are not that so. The objective based
review area has been heavily studied up to now where opinion mining is basically done using
predefined tags that are not context sensitive. On the other hand, subjective based reviews are
context sensitive and they depend on the polarity orientation of the term in the sentence. Opinion
mining on subjective reviews has not yet been explored in depth.
Unlike other reviews like movies and consumer electronic products, there hasn’t been any significant
work done in the area of opinion mining on book reviews, which can be categorised as subjective.
Contents of book reviews are subjective and each differs from the rest in various ways. Therefore, by
aggregating those different opinions on those reviews to a single perspective opinion on book
aspects may add more value to the book readers, in academic as well as commercial sectors.
This research focuses on introducing a fine-grained approach for opinion mining on online non-
scholarly book reviews, where an ontology reference model is used as an essential part of the
opinion extraction process by taking into account the relations between concepts. In other words, this
research exploits the benefits of using ontology structure for the mining of context sensitive book
reviews. Eventually the methodology adopted for mining the context sensitive reviews yielded quite
promising results when tested on amazon data set of book reviews.
Part 1 of a 2-day workshop to introduce style in technical communication. Presented to beginning students of technical communication on December 1, 2009 at Kyung Hee University in Suwon, South Korea.
Ontology based opinion mining for book reviewsfirzhan naqash
The recent burst in web usage has contributed to the growth of a number of various online reviews
like consumer product reviews, legal reviews, political reviews, movie reviews and book reviews.
Out of these reviews, some are context sensitive and others are not that so. The objective based
review area has been heavily studied up to now where opinion mining is basically done using
predefined tags that are not context sensitive. On the other hand, subjective based reviews are
context sensitive and they depend on the polarity orientation of the term in the sentence. Opinion
mining on subjective reviews has not yet been explored in depth.
Unlike other reviews like movies and consumer electronic products, there hasn’t been any significant
work done in the area of opinion mining on book reviews, which can be categorised as subjective.
Contents of book reviews are subjective and each differs from the rest in various ways. Therefore, by
aggregating those different opinions on those reviews to a single perspective opinion on book
aspects may add more value to the book readers, in academic as well as commercial sectors.
This research focuses on introducing a fine-grained approach for opinion mining on online non-
scholarly book reviews, where an ontology reference model is used as an essential part of the
opinion extraction process by taking into account the relations between concepts. In other words, this
research exploits the benefits of using ontology structure for the mining of context sensitive book
reviews. Eventually the methodology adopted for mining the context sensitive reviews yielded quite
promising results when tested on amazon data set of book reviews.
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A holistic lexicon based approach to opinion mining
1. Xiaowen Ding, Bing Liu and Philip Yu
Presenter: Quang Nguyen
Date: 2010.10.18
Saltlux Vietnam Development Center
2. Featured-based Opinion Mining Tasks
Task 1: Identify and extract object features F that have been
commented on by an opinion holder (e.g., a reviewer).
Task 2: Determine whether the opinions on the features F are
positive, negative or neutral.
Task 3: Group feature synonyms.
• Produce a feature-based opinion summary of multiple reviews.
This paper focuses on Task 2 assuming that features
have been discovered
2
3. Opinion Words
• Positive: beautiful, wonderful, good, amazing,
• Negative: bad, poor, terrible, cost someone an
arm and a leg (idiom).
One effective approach is to use opinion lexicon,
opinion words.
• Identify all opinion words in a sentence
• Aggregate these words to give the final opinion to
each feature.
3
4. Dictionary-based approaches
• Start from a seed opinion words
• Use Wordnet’s hierarchy and synsets to acquire
more opinion words
Corpus-based approaches: extract opinion
words from large corpora using syntactic
rules and co-occurrence patterns
Do not deal well with context dependent
words!
4
5. Improve lexicon-based approaches using
context dependent opinion words
• Negative: “The bedroom is very small”
• Positive: “The Nokia N3100 is so small as to be
put in any pockets”
Propose a function for aggregating multiple
opinion words in the same sentence
Consider explicit and implicit opinions
5
7. Opinion
on both sides of “and” should be
the same
• E.g., “This camera takes great pictures and has a
long battery life”.
Not likely to say:
• “This camera takes great pictures and has a short
battery life.”
7
8. Sometimes, one may not use an explicit
conjunction “and”.
• Same opinion in same sentence, unless there is a
“but”-like clause
• E.g., “The camera has a long battery life, which is
great”
8
9. Peopleusually express the same opinion
across sentences
• unless there is an indication of opinion change
using words such as “but” and “however”
• E.g., “The picture quality is amazing. The battery life is
long”
Not so natural to say:
• “The picture quality is amazing. The battery life is
short”
9
10. Opinion lexicon is far from sufficient. It needs
special handling:
• Negation/But Rule
• Non-negation contains negative word, e.g., “I like this camera
not just because it is beautiful”
• Not contrary, but has a “but”, e.g., ““I not only like the picture
quality of this camera, but also its size”
• …
10
11. Implicit
Feature is determined through
adjectives (implicit feature indicator)
• E.g., “This camera is very small”
“small” is indicator for “size”
• E.g., “This camera is very heavy”
• “heavy” is indicator for “weight”
11
12. An object O is an entity which can be a product,
person, event, organization, or topic
An object O is represented with a finite set of features,
F = {f1, f2, …, fn}.
• Each feature fi in F can be expressed with a finite set of words
or phrases Wi, which are synonyms.
Model of a review: An opinion holder j comments on a
subset of the features Sj F of object O.
• For each feature fk Sj that j comments on, he/she
chooses a word or phrase from Wk to describe the
feature, and
expresses a positive, negative or neutral opinion on fk.
12
13. Input: a pair (f, s), where f is a product feature and s is a
sentence that contains f.
Output: whether the opinion on f in s is pos, neg, or neut.
wi: opinion word
V: set of all opinion words
dis(wi, f): distance between wi and f
SO: semantic orientation of wi (+1, -1, 0)
13
16. Precision Recall F-Score
FBS
(M. Hu and B. Liu. Mining and 0.93 0.76 0.83
summarizing customer
reviews. KDD’04, 2004)
OPINE
(A-M. Popescu and O. Etzioni.
Extracting Product Features
0.86 0.89 0.87
and Opinions from Reviews. EMNLP-
05, 2005.)
Opinion Observer 0.92 0.91 0.91
(this paper)
16
17. Xiaowen Ding, Bing Liu, and Philip S. Yu, A Holistic
Lexicon-Based Approach to Opinion Mining, Proceedings
of the international conference on Web search and web
data mining, USA, 2008
17