Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals
Table 2: Results of TextBlob sentiment analyzer on the dataset D1
['[BOLD] Sentence Class', '[BOLD] Positive', '[BOLD] Negative', '[BOLD] Neutral']
We also explored whether a sentiment analyzer can be used as a baseline for identifying the class labels STRENGTH and WEAKNESS. It can be observed that distribution of positive and negative sentiments is almost similar in STRENGTH as well as SUGGESTION sentences, hence we can conclude that the information about sentiments is not much useful for our classification problem.