yunchao he icot2015
TRANSCRIPT
SENTIMENT CLASSIFICATION OF SHORT TEXTS BASED ON SEMANTIC CLUSTERING (PAPER ID: 61)
Yunchao HeChin-Sheng Yang, Liang-Chih Yu, K. Robert Lai and Weiyi Liu.
SENTIMENT ANALYSIS “unbelievably disappointing ” “Full of zany characters and richly applied satire, and some great plot twists”
“this is the greatest screwball comedy ever filmed” “ It was pathetic. The worst part about it was the boxing scenes.”
Sentiment Analysis Using NLP, statistics, or machine learning methods to extract, identify, or
otherwise characterize the sentiment content of a text unit Sometimes called opinion mining, although the emphasis in this case is
on extraction Other names: Opinion extraction 、 Sentiment mining 、 Subjectivity
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APPLICATION: PRODUCT REVIEWS
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WHY SENTIMENT ANALYSIS? Movie: is this review positive or negative? Products: what do people think about the new iPhone? Public sentiment: how is consumer confidence? Is despair increasing?
Politics: what do people think about this candidate or issue? Prediction: predict election outcomes or market trends from sentiment
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CHALLENGES IN SENTIMENT ANALYSIS People express opinions in complex ways In opinion texts, lexical content alone can be misleading Intra-textual and sub-sentential reversals, negation, topic change common
Rhetorical devices/modes such as sarcasm, irony, implication, etc.
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POLARITY CLASSIFICATION (E.G., [GO+ 09, PANG+ 04]) Tokenization Feature Extraction: n-grams, semantics, syntactic, etc. Classification using different classifiers
Naïve Bayes MaxEnt SVM
Drawback Sparsity Context independent
S1: I really like this movie[...0 0 1 1 1 1 1 0 0 ... ]
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S1: This phone has a good keypadS2: He will move and leave her for good
SEMANTIC CLUSTERINGBASIC IDEA
Using clustering algorithm to aggregate short text to form long clusters, in which each cluster has the same topic and the same sentiment polarity, to reduce the sparsity of short text representation and keep interpretation.
S1: it works perfectly! Love this productS2: very pleased! Super easy to, I love itS3: I recommend it
it works perfectly love this product very pleased super easy to I recommend
S1: [1 1 1 1 1 1 0 0 0 0 0 0 0]
S2: [0 0 0 1 0 0 1 1 1 1 1 1 0]
S3: [1 0 0 0 0 0 0 0 0 0 0 1 1]
S1+S2+S3: [...0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0...]7
CLUSTERING TRAINING DATA Training data labeled with positive and negative polarity K-means clustering algorithm is used to cluster positive and negative text separately. K-means, KNN, LDA…
works perfectly! Love this productcompletely useless, return policyvery pleased! Super easy to, I am pleasedwas very poor, it has failedhighly recommend it, high recommended!it totally unacceptable, is so bad
works perfectly! Love this productvery pleased! Super easy to, I am pleasedhighly recommend it, high recommended!
completely useless, return policywas very poor, it has failedit totally unacceptable, is so bad
Topical clusters8
ADVANTAGES OF CLUSTERING TEXT Topical consistency: texts in each cluster have similar topic Sparsity reduced: The representation of topical clusters is more dense than single text
Easy to apply the idea to other area
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TRAINING CLASSIFIERSClassifier: Multinomial Naive BayesProbabilistic classifier: get the probability of label given a clustered text
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Bayes’ theoryIndependent assumption
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UNLABELED TEXT CLASSIFICATIONTWO-STAGE-MERGING METHOD Given an unlabeled text , we use Euclidean distance to find the most similar positive cluster , and the most similar negative cluster
The sentiment of , is estimated according to the probabilistic change of the two clusters when merging with . (vs. KNN)
This merging operation is called two-stage-merging method, as each unlabeled text will be merged two times.0, | ( ) ( ) | | ( ) ( ) |( )
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EXPERIMENT Dataset: Stanford Twitter Sentiment Corpus (STS) Baseline: bag-of-unigrams and bigrams without clustering Evaluation Metrics: accuracy, precision, recall
The average precision and accuracy is 1.7% and 1.3% higher than the baseline method.
Methods Accuracy Precision Recall
Our Method 0.816 0.82 0.813
Bigrams 0.805 0.807 0.802
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CONCLUSION AND FUTURE WORKS We introduce a Clustering algorithm based method to reduce sparsity problem for sentiment classification of short text
This idea can be applied to other area The above method is just a prototype work and some technique can be used to improve the model, including clustering algorithms, distributed representation and the two-stage-merging method.
Future works: Expanding this model use top-n similar clusters. Use distributed representation. Some deep learning model.
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