Customer Emotions Recognition Using Facial and Textual Review

EasyChair Preprint 2658

Abstract

Emotion analysis of posts is still challenging because of the limited contextual information that they normally contain. We address this issue by constructing an emotional space as a feature representation matrix and words into the emotional space based on the semantic composition. To improve the performance of emotion analysis, we propose an Emotion Recognition on Amazon using a Python-based new Emotion-Semantic Enhanced Convolutional Neural Network (ECNN) Model. ECNN can use emoticon embedding as an emotional space projection operator. By projecting words into an emoticon space, it can help identify subjectivity, polarity, and emotion in micro blog environments. It is more capable of capturing emotion semantic than other models, so it can improve the emotion analysis performance. This project provides insights on the design of ECNN for sentimental analysis in other natural language processing tasks. Facial expressions give important clues about emotions. Therefore, several approaches have been proposed to classify human affective states. The features used are typically based on local spatial position or displacement of specific points and regions of the face, unlike the approaches based on audio, which uses global statistics of the acoustic features.

Keyphrases: Amazon Product Review, emotion recognition, text mining

@booklet{EasyChair:2658,
  author    = {Preeti Thakre and Pankaj Agarkar},
  title     = {Customer Emotions Recognition Using Facial and Textual Review},
  howpublished = {EasyChair Preprint 2658},
  year      = {EasyChair, 2020}}