NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets

Understand

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.