%0 Generic %1 Saif2013 %A Mohammad, Saif M. %A Kiritchenko, Svetlana %A Zhu, Xiao-Dan %D 2013 %J ArXiv %K sentiment_analysis twitter related_works_benchmark posted_with_chatgpt %T NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets %U https://www.semanticscholar.org/paper/506b0cad3d660a53364d622e49673ae5f95526c8 %V abs/1308.6242 %X 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 message (term-level task). Among submissions from 44 teams in a competition, our 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. We also generated two large word‐sentiment association lexicons, one from tweets 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 using freely available resources. 1

@JournalArticle{Saif2013, 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 message (term-level task). Among submissions from 44 teams in a competition, our 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. We also generated two large word‐sentiment association lexicons, one from tweets 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 using freely available resources. 1}, added-at = {2023-09-22T12:33:09.000+0200}, author = {Mohammad, Saif M. and Kiritchenko, Svetlana and Zhu, Xiao-Dan}, biburl = {https://www.bibsonomy.org/bibtex/225ddd8685269a80a690ceba8d885c434/tomvoelker}, day = 28, description = {A study by NRC-Canada that aimed to build a state-of-the-art model for sentiment analysis on tweets.}, interhash = {659eb7a5f0bddf3ed7aabbb5677214ec}, intrahash = {25ddd8685269a80a690ceba8d885c434}, journal = {ArXiv}, keywords = {sentiment_analysis twitter related_works_benchmark posted_with_chatgpt}, month = {8}, timestamp = {2023-09-22T12:33:09.000+0200}, title = {NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets}, url = {https://www.semanticscholar.org/paper/506b0cad3d660a53364d622e49673ae5f95526c8}, volume = {abs/1308.6242}, year = 2013 }