Action rules for sentiment analysis using Twitter
Angelina A. Tzacheva, Jaishree Ranganathan, Arunkumar Bagavathi · International Journal of Social Network Mining · 2020
Actionable patterns are interesting and usable knowledge mined from large datasets. Action rules are rules that describe the possible transition of objects from one state to another with respect to a decision attribute. In this work, we extract actionable recommendations in the form of action rules, that can be applied to social networking data in a scalable manner to achieve the desired user goals. We propose extraction of actionable patterns based on sentiment analysis of social network data. In sentiment analysis there are two approaches to determine the polarity: Corpus-based, and Lexicon-based. We use corpus-based sentiment analysis approach, where sentiment values are generated based on sentence structure rather than words. Results show actionable patterns in tweet data and provide suggestions on how to change sentiment of the tweet to a more positive one. The experiment is performed with Twitter data in a distributed environment using Hadoop MapReduce for scalability with large data.