Since Emotize’s sentiment analysis is polar (either Negative or Positive), the algorithm can either classify positive correctly, or incorrectly. For 77.9% of the corpus phrases, the text classifying algorithm categorized the corpus data correctly. The dataset corpus that this text classification algorithm was trained with for use on Emotize was introduced by Pang/Lee, with 3,800 corpus phrases. 1/3 of them (1,280 corpus sentences) were excluded from the training set of the classifier and the ROC curve was calculated with the remaining corpus sentences from the training examples. This allowed the true/false outcomes to be known, and for the accuracy rate to be calculated by comparing the two outcomes. This data illustrates that Emotize’s sentiment analysis machine learning algorithm has an accuracy rate of 77.9%, which is a 1.1% difference between the average human sentiment analysis detection accuracy rate to positive/negative texts, which is 79%.
Mood and Sentiment Polarity Classifier
Since Emotize’s sentiment analysis is polar (either Negative or Positive), the algorithm can either classify positive correctly, or incorrectly. For 77.9% of the corpus phrases, the text classifying algorithm categorized the corpus data correctly. The dataset corpus that this text classification algorithm was trained with for use on Emotize was introduced by Pang/Lee, with 3,800 corpus phrases. 1/3 of them (1,280 corpus sentences) were excluded from the training set of the classifier and the ROC curve was calculated with the remaining corpus sentences from the training examples. This allowed the true/false outcomes to be known, and for the accuracy rate to be calculated by comparing the two outcomes. This data illustrates that Emotize’s sentiment analysis machine learning algorithm has an accuracy rate of 77.9%, which is a 1.1% difference between the average human sentiment analysis detection accuracy rate to positive/negative texts, which is 79%. The raw, unparsed, and unorganized survey data used partly as the corpus data during the training process for this algorithm, is available here in the form of a ZIP file. Personality Analysis Conducted with the Five-Factor Model (FFM) Classifier
The corpus for the 5-factor psychometric machine learning algorithms were collected through a survey of 1,741 participants through the Emotize website. Emotize was able to build the 5-factor psychometric machine learning algorithm through this data. As in the Mood and Sentiment Polarity Analysis, 1/3 of the algorithm’s original training set corpus of the classifier was used in the ROC Curve.