Building a Sentiment Analysis Predictive Model - Lexicon Based Approach
This workflow uses a Kaggle Dataset including 14K customer tweets towards six US airlines (https://www.kaggle.com/crowdflower/twitter-airline-sentiment). Contributors annotated the valence of the tweets as positive, negative and neutral. In the lexicon based approach the number of words with a positive and a negative meaning are counted per Tweet. Based on these numbers a sentiment score is calculated and used to classify the tweets.
If you use this workflow, please cite: F. Villaroel Ordenes & R. Silipo, “Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications”, Journal of Business Research 137(1):393-410, DOI: 10.1016/j.jbusres.2021.08.036.
This workflow uses a Kaggle Dataset including 14K customer tweets towards six US airlines (https://www.kaggle.com/crowdflower/twitter-airline-sentiment). Contributors annotated the valence of the tweets as positive, negative and neutral. In the lexicon based approach the number of words with a positive and a negative meaning are counted per Tweet. Based on these numbers a sentiment score is calculated and used to classify the tweets.
If you use this workflow, please cite: F. Villaroel Ordenes & R. Silipo, “Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications”, Journal of Business Research 137(1):393-410, DOI: 10.1016/j.jbusres.2021.08.036.