Personality Traits Analysis among Social Media Influencers

  • Universal Sentence Encoder
  • Doc2Vec
  • LSTM

The Data:

Exploring the Data:

Pre-processing:

Models Analysis:

Universal Sentence Encoder:

  • It is an Unsupervised Learning model and hence there is no need for training data.
  • As it works based on Semantic similarity between statements, it performs well in capturing the core sentiment of the statements (Positive or Negative) although they aren’t the exact labels.
  • The programmer must be well versed and have a thorough understanding of the Sentiment Labels himself as to hardcode the Synonyms List for each Label without Redundancy.
  • Performance depends on the quality of the Synonyms.
  • It is not efficient for predicting Sentiment labels that are related to each other.

Doc2Vec:

  • Requires less or very basic understanding of the Sentiment labels.
  • Requires large amount of training data.
  • The model performance entirely depends on the scale of the training dataset.

LSTM:

  • The availability of Memory cells that store previous information and the flexibility to alter the learning rates.
  • Requires large amount of training data.
  • The model performance entirely depends on the scale of the training dataset.

Comparison:

Conclusion:

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