Maximizing Sentiment Detection Through Comprehensive Multimodal Data Fusion: Integrating CNN, RNN, LSTM

Production houses will benefit from the process of multimodal sentiment analysis by providing a holistic understanding of audience reaction by fusing information from text, audio, images, and video. This multi-dimensional view provides them with an opportunity to make better decisions in terms of marketing, content, and scheduling. It begins with using the IMDb dataset as the major source for training and also evaluating the models on structured data. IMDb dataset as a primary source for training and evaluating the models on structured data. It innovates multimodal approaches by proposing a different method based on the conversion of audio from YouTube videos into transcript text for classification. The proposed approach shall help in sustaining the reviews in multilingual languages. The sentiment analysis of movie reviews would help the production houses assess the reaction of the audiences beforehand, thereby providing an idea as to whether the film will turn out to be a box office bomb or a blockbuster. This approach classifies data more efficiently using deep learning architectures such as convolutional and recurrent neural networks. In all the models tested for this, the best efficiency was reached in classifying the positive and negative sentiments. Improving the text classification technique, this technology helps to draw more valuable insights from unstructured text data, hence adding value to lots of commercial applications in marketing, product value management, and social media influential analysis. It also provides practical guidance on how the industries that rely on sentiment analysis for optimisation and strategy alignments with their target audiences will make decisions.