Introduction/Background

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雨林木风

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Julie Kane Ahkter (kanej) Steven Soria (ssoriajr)
Sentiment Analysis: Facebook Status Messages
Final Project CS224N Abstract
While recent NLP-based sentiment analysis has centered around Twitter and product/service reviews, we believe it is possible to more accurately classify the emotion in Facebook status messages due to their nature. Facebook status messages are more succinct than reviews, and are easier to classify than tweets because their ability to contain more characters allows for better writing and a more accurate portrayal of emotions. We analyze the suitability of various approaches to Facebook status messages by comparing the performance of a Maximum Entropy ("MaxEnt") classifier, a MaxEnt classifier augmented with LabeledLDA ("LDA") data, a MaxEnt classifier augmented with part-of-speech ("POS") tagging, and a MaxEnt classifier augmented with both LDA and POS data. We classify both binary and multi-class sentiment labeling. In both cases, a MaxEnt classifier augmented with POS data performs the best, achieving an average binary classification F1 score of approximately 85% and an average multi-class F1 score of approximately 67%.
Introduction/Background

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