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Towards a Unified Sentiment Lexicon Based on Graphics Processing Units

Liliana Ibeth Barbosa-Santillan, Inmaculada Alvarez-de-Mon y-Rego
Department of Computer Science, Universidad de Guadalajara, Periferico Norte 799, Modulo L-308, Los Belenes, 45100 Guadalajara, JAL, Mexico
Mathematical Problems in Engineering, Volume 2014, Article ID 429629, 19 pages, 2014

@article{barbosa2014towards,

   title={Towards a Unified Sentiment Lexicon Based on Graphics Processing Units},

   author={Barbosa-Santill{‘a}n, Liliana Ibeth and others},

   journal={Mathematical Problems in Engineering},

   volume={2014},

   year={2014},

   publisher={Hindawi Publishing Corporation}

}

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This paper presents an approach to create what we have called a Unified Sentiment Lexicon (USL). This approach aims at aligning, unifying, and expanding the set of sentiment lexicons which are available on the web in order to increase their robustness of coverage. One problem related to the task of the automatic unification of different scores of sentiment lexicons is that there are multiple lexical entries for which the classification of positive, negative, or neutral depends on the unit of measurement used in the annotation methodology of the source sentiment lexicon. Our USL approach computes the unified strength of polarity of each lexical entry based on the Pearson correlation coefficient which measures how correlated lexical entries are with a value between 1 and -1, where 1 indicates that the lexical entries are perfectly correlated, 0 indicates no correlation, and -1 means they are perfectly inversely correlated and so is the UnifiedMetrics procedure for CPU and GPU, respectively. Another problem is the high processing time required for computing all the lexical entries in the unification task. Thus, the USL approach computes a subset of lexical entries in each of the 1344 GPU cores and uses parallel processing in order to unify 155802 lexical entries. The results of the analysis conducted using the USL approach show that the USL has 95.430 lexical entries, out of which there are 35.201 considered to be positive, 22.029 negative, and 38.200 neutral. Finally, the runtime was 10 minutes for 95.430 lexical entries; this allows a reduction of the time computing for the UnifiedMetrics by 3 times.
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