Abstract
Previous works on Polish sentiment dictionaries revealed the superiority of machine learning on vectors created from word contexts (concordances or word co-occurrence distributions), especially compared to the SO-PMI method (semantic orientation of pointwise mutual information). This paper demonstrates that this state-of-the-art method could be improved upon when extending the vectors by word embeddings, obtained from skip-gram language models. Specifically, it proposes a new method of computing word sentiment polarity using feature sets composed of vectors created from word embeddings and word co-occurrence distributions. The new technique is evaluated in a number of experimental settings.
This work was funded by the National Science Centre of Poland grant nr UMO-2012/05/N/ST6/03587.
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Notes
- 1.
Micro means to calculate metrics globally by counting the total true positives, false negatives and false positives. Thus, it takes into account label imbalance. The formula is: \(F1 = 2 * (precision * recall) / (precision + recall)\) .
- 2.
Macro means to calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
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Wawer, A. (2015). Sentiment Dictionary Refinement Using Word Embeddings. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_20
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