Sentiment analysis and affect detection algorithms are generally based on annotated data, structured into dictionaries, ontologies or word nets. Among other research problems, two issues are considered very important in this field: 1) word sense disambiguation and 2) accuracy of affect detection. Most of the current approaches use annotated resources based on word nets. Their structure, founded on synonymic relations, makes the disambiguation process very difficult. Our model uses contextonyms, which simplify the decision process. Therefore, the disambiguation issue is transformed into a context matching problem. The second focus is on the manual annotation of the data followed by a semantic valence propagation. This approach enables the generation of new affective labels from a set of initial ones, through the expansion process. Unfortunately, this is usually done to the detriment of precision. We use an existing linguistic resource, SentiWordNet, which is one of the largest dictionaries available for sentiment analysis. Using our disambiguation model, we manage to solve all the SentiWordNet ambiguities and inconsistencies, which increases the accuracy of the classification process. This is the first of our major contributions. Second, we manage to reduce the disagreement percentage computed against well known linguistic resources to less than half of the original rate.
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