Emotion Recognition from Blog Comments Based Automatically Generated Datasets and Ensemble Models

Abstract

Emotions have a large influence on product loyalty and found that an individual have a positive emotional relationship with identified product, trust in the company and make purchases. The customer's decision is not founded on their facts/ information, but they rely on their emotions to get brand loyalty decisions. However, companies are struggling to adapt this strategy even with the huge amount of data they acquired, their utilization of this data as marketing influence and ongoing personalization tactics is limited due to the problem of analyzing voluminous data. This work offers new approach for automated categorization of emotions from textual data and was based on a sentence level utilizing the language translation machine and applying of learning machines and ensemble models. The experiment reveals that the proposed automatic emotion classifier performs well in classifying angry comments both in training and testing evaluation periods. Furthermore, the proposed emotion classifier produces a result of 75% accuracy under ensemble Naïve Bayes (NB) in the classification of sentence emotion during training period. Although during the actual testing period of the test data set, the proposed system was able to correctly classify 76% emotions of the sentences. Some of the reasons and causes for misclassification are analyzed and presented in this paper.

Identifying Chinese social media users' need for affect from their online behaviors

“…Different from emotion and emotion regulation, NFA focuses on people's attitude toward emotion itself (2), and it is a psychological trait related to emotion. Although we do not find any research to date specifically on NFA identification based on network behaviors, many studies have found that users' emotions could be identified based on the blogs and blog comments they posted on social media [e.g., (33,34)].…”