NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets
This work provides improved tools for analyzing sentiment in social media data, but it is incremental as it builds on existing methods with new features.
The paper tackled sentiment analysis of tweets by building two SVM classifiers for message-level and term-level tasks, achieving state-of-the-art F-scores of 69.02 and 88.93, respectively.
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.