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The Impact Of Natural Language Processing-Based Textual Analysis Of Social Media Interactions On Decision Making

Organizations typically use sentiment analysis-based systems, or even resort to simple manual analysis, to try to derive useful meaning from the public digital “chatter” of their customers. Motivated by the need for a more accurate way to qualitatively mine valuable productand brandoriented consumer-generated text, this paper experimentally tests the ability of an NLP-based analytics approach to extracting knowledge from highly unstructured text. Results indicate that for detecting problems from social media data, natural language processing outperforms sentiment analysis. Surprisingly, the experiment indicates that sentiment analysis is not only no better than manual analysis of social media data toward the goal of supporting organizational decision-making, but may even prove disadvantageous to such efforts.