Score embedding, a CNN initialized with per-class word frequencies, reaches about 69% accuracy on ACA tweets and 46% on SST, but its central public-opinion finding is confounded by the imbalanced training labels.
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets
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abstract
In this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of a term within a submissions stood first in both tasks on tweets, obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. We implemented a variety of surface-form, semantic, and sentiment features. with sentiment-word hashtags, and one from tweets with emoticons. In the message-level task, the lexicon-based features provided a gain of 5 F-score points over all others. Both of our systems can be replicated us available resources.
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Twitter Sentiment on Affordable Care Act using Score Embedding
Score embedding, a CNN initialized with per-class word frequencies, reaches about 69% accuracy on ACA tweets and 46% on SST, but its central public-opinion finding is confounded by the imbalanced training labels.