1. Sentiment analysis of tweets using Neural Networks
Adri´an Palacios
Universidad Polit´ecnica de Valencia
June 6th, 2013
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2. Introduction
The objective of this work is:
• To use Neural Networks (using the April toolkit) for the polarity
classification of tweets.
• To check how NNs behave when applying different techniques for
preprocessing the data.
• We don’t look for good results, we are just experimenting with
these techniques.
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3. Preprocessing of tweets
Prior to the training of NNs, we need to obtain a feature vector
representation for the samples (tweets):
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4. Preprocessing techniques
To achieve this, we create a bag of words after applying one of the
following preprocessing techniques:
1. Unigrams.
2. Bigrams.
3. Stemming.
4. Lemmatization.
5. Part-of-Speech tagging.
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5. Stemming
Stemming: A process that chops off the suffixes of a given word
following some predefined rules.
Examples:
• Stem(run): run.
• Stem(ran): ran.
• Stem(running): run.
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6. Lemmatization
Lemmatization: A process that determines the lemma (canonical
form of the lexeme) of a given word.
Examples:
• Lemma(run): run.
• Lemma(ran): run.
• Lemma(running): run.
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7. PoS tagging
PoS tagging: The assignation Part-of-Speech tags to the words of a
given sentence.
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8. Learning techniques
The polarity classification will be made:
• Using a Multilayer Perceptron with a single layer,
• after 5-fold cross-validation technique,
• and an ensemble of the resulting MLPs.
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9. Hyper-parameter search
We will perform a random search for hyper-parameter optimization
instead of a grid search.
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10. Ensemble methods
After training is done, since we use 5-fold cross-validation, we get 5
MLPs for each set of parameters.
To be consistent, we merge these 5 classifiers into a single one using
the bootstrap aggregating method (votes have equal weight) for the
ensemble.
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11. Corpus
We will work with the corpus provided at the 2012 edition of the
Workshop on Sentiment Analysis at SEPLN.
Training Test
Samples 7219 60798
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12. Training results
Accuracy of the validation set classification:
3 levels 5 levels
Unigrams 54.44 45.62
Bigrams 54.09 39.99
Stemming 62.34 47.49
Lemmatization 61.60 46.75
PoS-tagging 52.58 38.40
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13. Test results
Accuracy of the test set classification (average and ensemble):
3 levels 5 levels
Unigrams 32.13 26.12
Bigrams 32.39 28.21
Stem. 32.34 26.81
Lemma. 31.84 26.18
PoS-tag. 35.22 35.22
3 levels 5 levels
Unigrams 32.16 26.52
Bigrams 32.32 29.32
Stem. 32.23 27.16
Lemma. 31.80 26.49
PoS-tag. 35.22 35.22
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14. Conclusions
Results are bad, but we can improve by:
• Using more complex techniques for preprocessing.
• Using more complex models for learning.
• Exploring more values for random hyper-parameter search.
• Learning from PoS tagged tweets in a different way.
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15. Questions?
The tools used for the experiments can be found at:
• The NLTK: nltk.org
• Freeling: nlp.lsi.upc.edu/freeling
• The April toolkit: github.com/pakozm/april-ann
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