YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context

@article{Wllmer2013YouTubeMR,
  title={YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context},
  author={Martin W{\"o}llmer and Felix Weninger and Tobias Knaup and Bj{\"o}rn Schuller and Congkai Sun and Kenji Sagae and Louis-Philippe Morency},
  journal={IEEE Intelligent Systems},
  year={2013},
  volume={28},
  pages={46-53}
}
This work focuses on automatically analyzing a speaker's sentiment in online videos containing movie reviews. In addition to textual information, this approach considers adding audio features as typically used in speech-based emotion recognition as well as video features encoding valuable valence information conveyed by the speaker. Experimental results indicate that training on written movie reviews is a promising alternative to exclusively using (spoken) in-domain data for building a system… 

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References

SHOWING 1-10 OF 16 REFERENCES
Towards multimodal sentiment analysis: harvesting opinions from the web
TLDR
This paper addresses the task of multimodal sentiment analysis, and conducts proof-of-concept experiments that demonstrate that a joint model that integrates visual, audio, and textual features can be effectively used to identify sentiment in Web videos.
A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
TLDR
A novel machine-learning method is proposed that applies text-categorization techniques to just the subjective portions of the document, which greatly facilitates incorporation of cross-sentence contextual constraints.
MovieClouds: content-based overviews and exploratory browsing of movies
TLDR
This paper presents and evaluates MovieClouds, an interactive web application designed to access, explore and visualize movies based on the information conveyed in the different tracks or perspectives of its content, with a special focus on the emotional dimensions expressed in the movies or felt by the viewers.
Sentic Computing for social media marketing
TLDR
This work uses Sentic Computing, a multi-disciplinary approach to opinion mining and sentiment analysis, to semantically and affectively analyze text and encode results in a semantic aware format according to different web ontologies to represent this information as an interconnected knowledge base.
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
TLDR
This work extends to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline.
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
TLDR
A simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (Thumbs down) if the average semantic orientation of its phrases is positive.
Multilingual Subjectivity: Are More Languages Better?
TLDR
This paper explores the integration of features originating from multiple languages into a machine learning approach to subjectivity analysis, and aims to show that this enriched feature set provides for more effective modeling for the source as well as the target languages.
Opensmile: the munich versatile and fast open-source audio feature extractor
TLDR
The openSMILE feature extraction toolkit is introduced, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities and has a modular, component based architecture which makes extensions via plug-ins easy.
...
...