This paper presents a simple, robust and (almost) unsupervised dictionary-based method, qwn-ppv (Q-WordNet as Personalized PageRanking Vector) to automatically generate polarity lexicons. We show that qwn-ppv outperforms other automatically generated lexicons for the four extrinsic evaluations presented here. It also shows very competitive and robust results with respect to manually annotated ones. Results suggest that no single lexicon is best for every task and dataset and that the intrinsic evaluation of polarity lexicons is not a good performance indicator on a Sentiment Analysis task. The qwn-ppv method allows to easily create quality polarity lexicons whenever no domain-based annotated corpora are available for a given language.
The most popular manually-built polarity lexicon is part of the General Inquirer @cite_8 , and consists of 1915 words labelled as positive'' and 2291 as negative''. Taboada manually created their lexicons annotating the polarity of 6232 words on a scale of 5 to -5. Liu , starting with Hu and Liu , have along the years collected a manually corrected polarity lexicon which is formed by 4818 negative and 2041 positive words. Another manually corrected lexicon @cite_21 is the one used by the Opinion Finder system @cite_13 and contains 4903 negatively and 2718 positively annotated words respectively.
@cite_21 @cite_13 @cite_8
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This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. The learned patterns are then used to identify more subjective sentences. The bootstrapping process learns many subjective patterns and increases recall while maintaining high precision. This paper presents a new approach to phrase-level sentiment analysis that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions. With this approach, the system is able to automatically identify the contextual polarity for a large subset of sentiment expressions, achieving results that are significantly better than baseline.