Download PDFOpen PDF in browser

Video-Based Sentiment Analysis with hvnLBP-TOP Feature and bi-LSTM

EasyChair Preprint 657

2 pagesDate: November 29, 2018

Abstract

In this paper, we propose a new feature extraction method called hvnLBP-TOP for video-based sentiment analysis. Furthermore, we use principal component analysis (PCA) and bidirectional long short term memory (bi-LSTM) for dimensionality reduction and classification. We achieved an average recognition accuracy of 71.1% on the MOUD dataset and 63.9% on the CMU-MOSI dataset.

Keyphrases: Sentiment Analysis, cmu mosi dataset, facial expression recognition, feature extraction, machine learning, moud dataset, video based sentiment analysis, video sentiment analysis

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:657,
  author    = {Haoran Li and Hua Xu},
  title     = {Video-Based Sentiment Analysis with hvnLBP-TOP Feature and bi-LSTM},
  howpublished = {EasyChair Preprint 657},
  year      = {EasyChair, 2018}}
Download PDFOpen PDF in browser