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Two steps for the classification
Two steps for the classification
Two steps for the classification
Two steps for the classification
Issue
Issue
Overview
Overview
Overview
Overview
Overview
Overview
Overview
Overview
Overview
Overview
Word Sub-Sequence
Word Sub-Sequence
Dependency Sub-Tree
Dependency Sub-Tree
Dependency Sub-Tree
Dependency Sub-Tree
Results (1/2)
Results (1/2)
Results (2/2)
Results (2/2)
Examples of Weighed Patterns
Examples of Weighed Patterns
A Word Sequence = A Clause
A Word Sequence = A Clause
A Word Sequence = A Clause
A Word Sequence = A Clause
A Word Sequence = A Clause
A Word Sequence = A Clause
A Word Sequence = A Clause
A Word Sequence = A Clause
References
References
References
References
References
References
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Sentiment Classification using Word Sub-Sequences and Dependency Sub-Trees Pacific-Asia Knowledge Discovery and Data Mining May, 18th-20th, 2005 Shotaro Matsumoto, Hiroya Takamura and Manabu Okumura Tokyo Institute of Technol

содержание презентации «Sentiment Classification using Word Sub-Sequences and Dependency Sub-Trees Pacific-Asia Knowledge Discovery and Data Mining May, 18th-20th, 2005 Shotaro Matsumoto, Hiroya Takamura and Manabu Okumura Tokyo Institute of Technol.ppt»
Сл Текст Сл Текст
1Sentiment Classification using Word 16reviews and 690 negative reviews Written
Sub-Sequences and Dependency Sub-Trees in English 3-fold cross-validation Dataset
Pacific-Asia Knowledge Discovery and Data 2: used in [Pang 04] 1000 positives and
Mining May, 18th-20th, 2005 Shotaro 1000 negatives Written in English 10-fold
Matsumoto, Hiroya Takamura and Manabu cross-validation.
Okumura Tokyo Institute of Technology. 17Features. We employ the following
2Table of Contents. 1. Motivation 2. features and their combinations for the
Our Approach 3. Experiments 4. Result and classification Bag-of-words features
Discussion 5. Conclusion and Future Work. Unigram (ex: “good”, “film”): uni Unigram
3Table of Contents. Motivation patterns appear in at least 2 distinct
Background Document Sentiment sentences Bigram (ex: “very good”, “film
Classification Early Studies Issue is”): bi Bigram patterns appear in at
Objective 2. Our Approach 3. Experiments least 2 distinct sentences Frequent
4. Results and Discussions 5. Conclusion sub-pattern features Word Sub-Sequence:
and Future Work. seq Dependency Sub-tree: dep Features of
4Background. Online grass-roots reviews lemmatized words As in the extraction of
are rapidly increasing Contain useful the features uni, bi, seq, dep, also
reputation There are so many such extract unil, bil, seql, depl.
documents that we cannot read them all 18Classifiers and Tests (1/2).
Mining reputation from such documents is Classifier Method: SVMs, binary classifier
important. based on supervised learning Kernel
5Document sentiment classification. a function: linear kernel Performance
task to classify an overall document closely depends on its learning parameter
according to the positive or negative C (called soft margin parameter) ? We
polarity of its opinion (desirable or carry out three kind of experiments.
undesirable). 19Classifiers and Tests (2/2). Test 1:
6Two steps for the classification. fix C as 1 The result is used for
Feature extraction convert a document to a comparison to the early studies Test 2:
feature vector, which preserves features best accuracy with C ? {e-2.0, e-1.5, …,
of the original document 2. Binary e2.0} Observe potential performance of
classification Classify the feature vector features Use the result for finding the
to positive or negative sentiment best effective combination of bag-of-words
polarity. features Test 3: predict a proper value of
7Early Studies. [Pang 02] Features: C from training data Observe practical
unigrams in the document Classifier: Na?ve performance of features.
Bayes, ME Model, Support Vector Machines 20Table of Contents. 1. Motivation 2.
(SVMs) Showed SVMs is superior to others Our Approach 3. Experiments 4. Results and
[Pang 04] Features: unigrams obtained from Discussion Results Discussion 5.
the summary Classifier: SVMs [Mullen 04] Conclusion and Future Work.
Features: unigrams, unigrams of lemmatized 21Results (1/2). Results for dataset 1
words, prior knowledge from Internet and vs Pang 82.9% ? 87.3% (error reduction:
thesaurus Classifier: SVMs Get better 26%) vs Mullen 84.6% ? 87.3% (error
results than [Pang 02]. reduction: 18%).
8Issue. Features in early studies A 22Results (2/2). Results for dataset 2
document is represented as a bag-of-words, vs Pang 87.1% ? 92.9% (error reduction:
where a text is regarded as a set of words 45%).
? Word order and syntactic relations 23Discussion. From the results of the
between words in a sentence, intuitively test1, our method proved to be effective
important for the classification, are Accuracy by features: bow + dep ? bow +
discarded. dep + seq (93%) >> bow + seq (89%)
9Objective. We propose a method for > bow (87%) Lemmatized features are not
extracting word order and syntactic always more effective than the original
relations as features. We use frequent ones.
sub-patterns in sentences as these 24Table of Contents. 1. Motivation 2.
features. Our Approach 3. Experiments 4. Results and
10Table of Contents. 1. Motivation 2. Discussion 5. Conclusion and Future Work
Our Approach Overview Word Sub-Sequence Conclusion Future Work.
Dependency Sub-Tree Frequent Sub-pattern 25Conclusion. We proposed a method for
3. Experiments 4. Result and Discussion 5. incorporating word order and syntactic
Conclusion and Future Work. relations between words in a sentence into
11Overview. We use a word sequence and a document sentiment classification by using
dependency tree as structured frequent word sub-sequences and dependency
representations of a sentence We extract sub-trees as features. Experimental
frequent sub-patterns from sentences as results on movie review datasets show that
features for the classification. our classifiers obtained the best results
12Word Sub-Sequence. A word sequence S yet published using these datasets.
Just a sequence of words which represents 26Future Work (1/2).
a sentence preserves word order in a Negative/Interrogative Sentence
sentence A word sub-sequence S’ of a word affirmative sentence : This film is good.
sequence S Obtained by removing zero or (1) Negative sentence: This film is not
more words from the original sequence good. (2) Interrogative sentence: Is this
Preserve the word order of the original film good? (3) All sub-patterns in
sentence. sentence (1) are also contained in
13Dependency Sub-Tree. A dependency tree sentence (2). Similarly, there is a large
D Expresses dependency between words in overlap of patterns between (1) and (3).
the sentence by child-parent relationships Distinguishing these sentence-types would
of nodes Preserves syntactic relations solve these problems.
between words in the sentence A dependency 27Future Work (2/2). Incorporating
sub-tree D’ of a dependency tree D discourse structures in a document Example
Obtained by removing zero or more nodes (positive movie review) The scenario is
from the original tree Preserves syntactic simplistic. But I love this film. By a
relations between words in the original word “but”, we would know that “I love
sentence. this film” is a more important sentence
14Frequent Sub-Pattern. The number of than “The scenario is simplistic” in the
all sub-patterns (subsequences or sense of sentiment classification.
subtrees) is too large ? Use only frequent 28Thank you.
sub-patterns Definition A sentence 29Examples of Weighed Patterns.
contains a pattern if and only if the Positive(+) weight shows positive
pattern is a subsequence or a subtree of sentiment polarity Negative(-) weight
the sentence A support of a pattern is the shows negative sentiment polarity The
number of sentences containing the pattern absolute value of each weight indicates
in a dataset If a support of a pattern is how large the contribution of the feature
a given support threshold or more, the is.
pattern is frequent. (In this experiment, 30A Word Sequence = A Clause. Sentences
we fixed support threshold to 10.) As are too long to be used for mining
implementations for mining frequent frequent sub-sequences Instead of
sub-patterns, we use Kudo’s Prefixspan and sentences, we used clauses of sentences as
FREQT. word sequences As in the figure on the
15Table of Contents. 1. Motivation 2. right, We split a sentence to a main
Our Approach 3. Experiments Movie review clause and subordinate clauses with
dataset Features Classifiers and Tests 4. information of parse tree In addition, we
Result and Discussion 5. Conclusion and removed stopwords. Conjunction,
Future Work. preposition, number, etc…
16Movie review dataset. Dataset 1: used 31References. [Pang 02] [Pang 04]
in [Pang 02], [Mullen 04] 690 positive [Mullen 04].
Sentiment Classification using Word Sub-Sequences and Dependency Sub-Trees Pacific-Asia Knowledge Discovery and Data Mining May, 18th-20th, 2005 Shotaro Matsumoto, Hiroya Takamura and Manabu Okumura Tokyo Institute of Technol.ppt
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Sentiment Classification using Word Sub-Sequences and Dependency Sub-Trees Pacific-Asia Knowledge Discovery and Data Mining May, 18th-20th, 2005 Shotaro Matsumoto, Hiroya Takamura and Manabu Okumura Tokyo Institute of Technol

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