[Air-L] NLP for sentiment analysis of social media comment

[Astvansh, Vivek]

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 at listserv.aoir.org> on behalf of Nina Lasek <Nina.Lasek1 at hotmail.com>
Sent: Saturday, July 14, 2018 10:51 AM
To: air-l at 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
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