The challenge of sentiment analysis consists in automatically determining whether a text is positive or negative in tone. Part of the difficulty in this task stems from the fact that the same words or sentences can have very different sentimental meaning given their context. In our work, we further focus on news articles, which tend to use a more neutral vocabulary, as opposed to the emotionally charged vocabulary of opinion pieces such as editorials, reviews, and blogs. In this paper, we use MALLET (Machine Learning for Language Toolkit) to implement and train several algorithms for sentiment analysis, and run experiments to compare and contrast them.
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