An Empirical Study of Skip Gram Features and Regularization for Learning on Sentiment Analysis
The problem of deciding the overall sentiment of a user review is usually treated as a text classification problem. The simplest machine learning setup for text classification uses a unigram bag of words feature representation of documents, and this has been shown to work well for a number of tasks such as spam detection and topic classification. However, the problem of sentiment analysis is more complex and not as easily captured with unigram (single word) features. Bigram and trigram features capture certain local context and short distance negations thus outperforming unigram bag of words features for sentiment analysis. But higher order n gram features are often overly specific and sparse, so they increase model complexity and do not generalize well.
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