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Summary
This summary is machine-generated.

This study compares deep learning features and crowd-sourced keywords for social media sentiment analysis. Human-annotated data significantly improves artificial intelligence model development for opinion mining.

Keywords:
collective intelligencecrowdsourcingdeep learningopinion miningsentiment analysissocial media messages

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Area of Science:

  • Computational linguistics
  • Social media analytics
  • Artificial intelligence

Background:

  • Big data and AI enable real-time information mining from vast user-generated content.
  • Social media marketing increasingly relies on opinion mining, with platforms like Google and Facebook developing proprietary tools.
  • Current research predominantly focuses on Twitter sentiment analysis due to API accessibility, overlooking more popular platforms like Facebook and Instagram.

Purpose of the Study:

  • To compare low-level features for sentiment analysis, including deep learning methods (fastText, Doc2Vec) and crowd-sourced keywords (crowd lexicon).
  • To evaluate the effectiveness of these features and various machine learning models for sentiment analysis on tweets and Facebook comments.
  • To investigate the impact of human annotation on developing effective AI tools for opinion mining.

Main Methods:

  • Feature extraction using deep learning models (fastText, Doc2Vec) and a crowdsourcing platform for keyword generation (crowd lexicon).
  • Sentiment analysis model development using various machine learning algorithms.
  • Comparative analysis of feature types and machine learning methods on tweet and Facebook comment datasets.

Main Results:

  • Deep learning features and crowd lexicons show varying effectiveness in sentiment analysis.
  • Machine learning models trained on human-annotated data demonstrate superior performance.
  • The study highlights the value of the 'learning by example' paradigm through human annotation.

Conclusions:

  • Human annotation of even a small data portion is crucial for developing effective AI-driven sentiment analysis tools.
  • Integrating crowd-sourced insights alongside deep learning features enhances opinion mining capabilities.
  • Future research should explore opinion mining on more popular social media platforms beyond Twitter.