Sentiment analysis of tweets using Neural Networks

Adrian Palacios

Universidad Politecnica de Valencia

June 6th, 2013

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Introduction

The objective of this work is:

• To use Neural Networks (using the April toolkit) for the polarityclassification of tweets.

• To check how NNs behave when applying different techniques forpreprocessing the data.

• We don’t look for good results, we are just experimenting withthese techniques.

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Preprocessing of tweets

Prior to the training of NNs, we need to obtain a feature vectorrepresentation for the samples (tweets):

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Preprocessing techniques

To achieve this, we create a bag of words after applying one of thefollowing preprocessing techniques:

1. Unigrams.

2. Bigrams.

3. Stemming.

4. Lemmatization.

5. Part-of-Speech tagging.

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Stemming

Stemming: A process that chops off the suffixes of a given wordfollowing some predefined rules.

Examples:

• Stem(run): run.

• Stem(ran): ran.

• Stem(running): run.

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Lemmatization

Lemmatization: A process that determines the lemma (canonicalform of the lexeme) of a given word.

Examples:

• Lemma(run): run.

• Lemma(ran): run.

• Lemma(running): run.

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PoS tagging

PoS tagging: The assignation Part-of-Speech tags to the words of agiven sentence.

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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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Hyper-parameter search

We will perform a random search for hyper-parameter optimizationinstead of a grid search.

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Ensemble methods

After training is done, since we use 5-fold cross-validation, we get 5MLPs for each set of parameters.

To be consistent, we merge these 5 classifiers into a single one usingthe bootstrap aggregating method (votes have equal weight) for theensemble.

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Corpus

We will work with the corpus provided at the 2012 edition of theWorkshop on Sentiment Analysis at SEPLN.

Training Test

Samples 7219 60798

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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 47.49Lemmatization 61.60 46.75

PoS-tagging 52.58 38.40

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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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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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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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