Emotion Recognition and Classification of Film Reviews Based on Deep Learning and Multimodal Fusion

• Published in: Wireless communications and mobile computing Vol. 2022; pp. 1 - 10
• Main Authors: Na, Risu, Sun, Ning
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 05.08.2022; John Wiley & Sons, Inc
• Subjects: Artificial neural networks; Classification; Culture; Datasets; Deep learning; Dictionaries; Emotion recognition; Emotions; Industrial development; Machine learning; Neural networks; Noise tolerance; Semantics; Sentiment analysis; China
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/2024352

In terms of cross-cultural exchanges, the film is not only an important embodiment of a country’s cultural soft power but also the most direct and favorable way of communication. The advent of the all-around well-off era has propelled people’s demand for spiritual, cultural, and entertainment which promotes the vigorous development of the film culture industry. The expansion and development of China’s film market, domestic films, are a reflection and extension of China’s culture and ideas. It plays an extremely important role in enhancing cultural self-confidence and cultural output. In order to better grasp the emotional tendency of the audience, understand the viewing needs, and put forward suggestions on domestic film production, it is very necessary to analyze the emotion of film reviews and dig deep into semantics. Since the evaluation of film works considers many factors that are complex and changeable, the choice of model plays a significant role in the process of emotion analysis. The deep learning model represented by a deep neural network has high tolerance to sentence noise, has strong information discrimination, and features self-learning ability. It also has great advantages in emotion classification tasks. This study conducts an in-depth study and research on the traditional emotion analysis methods and finally puts forward an effective emotion analysis framework that combines the traditional emotion analysis method and deep learning network. This framework enhances the text vectorization representation and emotion classification model by performing emotion analysis. The effectiveness is verified by corresponding experiments which justify the superiority of the approach.