Hi, Nina: Yes; some scholars (particularly in the past, when field-specific dictionaries, such as one in finance, were not available) used their own supervised machine-learning method. This method comprised: (a) using human beings (e.g., MTurkers) to classify text into positive, negative, and neutral sentiment, (b) using this human classification to train a classifier (Lasso or support vector machine), and lastly, (c) using the trained model to classify the holdout sample. As you can guess, this is quite some work and perhaps not required when the relevant dictionary is readily available and you have a program that uses this dictionary to classify text. You can search Google Scholar to find articles that use human annotations and machine-learning classifiers and follow this supervised machine-learning approach. Best wishes! Vivek Astvansh Assistant Professor of Marketing Kelley School of Business, Indiana University Bloomington ________________________________________ From: Air-L <air-l-bounces@listserv.aoir.org> on behalf of Nina Lasek <Nina.Lasek1@hotmail.com> Sent: Saturday, July 14, 2018 10:51 AM To: air-l@listserv.aoir.org Subject: [Air-L] NLP for sentiment analysis of social media comment Dear all, most sentiment analyses of social media comments I know use dictionary-based approaches (e.g. sentigstrength). However, I am wondering if researchers also use Natural Language Processing approaches to examine the sentiment of social media comments; I am still an absolute beginner in this field, hence I would be very glad if somebody could point me to useful papers/tutorials/software for doing NLP based sentiment analyses? Many thanks, Nina _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org Join the Association of Internet Researchers: http://www.aoir.org/

Dear Nina, These papers were very helpful in my work with sentiment analysis Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011 <https://doi.org/10.1016/j.asej.2014.04.011> Balahur, A. (2013). Sentiment analysis in social media texts. In Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 120–128). Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000001 <https://doi.org/10.1561/1500000001> Taboada, M. (2016). Sentiment Analysis: An Overview from Linguistics. Annual Review of Linguistics, 2(1), 325–347. https://doi.org/10.1146/annurev-linguistics-011415-040518 <https://doi.org/10.1146/annurev-linguistics-011415-040518> Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics, 37(2), 267–307. Sergei Pashakhin Laboratory for Internet Studies, National Research University Higher School of Economics

On 14 Jul 2018, at 17:51, Nina Lasek <Nina.Lasek1@hotmail.com> wrote:

Dear all,

most sentiment analyses of social media comments I know use dictionary-based approaches (e.g. sentigstrength). However, I am wondering if researchers also use Natural Language Processing approaches to examine the sentiment of social media comments; I am still an absolute beginner in this field, hence I would be very glad if somebody could point me to useful papers/tutorials/software for doing NLP based sentiment analyses?

Many thanks, Nina _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org

Join the Association of Internet Researchers: http://www.aoir.org/