Movie industry has turned huge today, with a lot of money put at stake by the producer. Along with this, marketing strategies can be planned and improved dynamically according to the sentiments of the users available through their reviews on pre-release events like trailer, music launch, and other promotions. Better marketing can guarantee the producer at least a good opening whatever the story might be. This is where sentiment analysis becomes useful. Analyzing the sentiments of the reviews have been worked upon since long, and the algorithms for sentiment polarity classification used include tf-idf, word2vec and doc2vec.
Doc2Vec provides pretty high accuracy each time, considering the area of sentiment polarity classification. But there are two limitations to it namely high space complexity to store paragraph vectors and high running time. In this paper, both these limitations are overcome by using a modified approach built on top of word2vec algorithm, which improves the classification accuracy considerably as compared to word2vec and gives comparable and sometimes even better results than Doc2Vec.
In this project, in the first phase, gross is predicted by taking the attributes that the producers have, just after finishing the shooting of the movie, which can help them to plan the marketing strategies initially. In the second phase, sentiment analysis of the pre-release reviews is done at regular intervals using our proposed modified approach on word2vec and is compared with other techniques, which will help producers to plan and change their marketing strategies later on and will guide the distributors as to which movies are worth investing, considering the interest of people.
For more information, please go through the paper provided on the link below: https://link.springer.com/chapter/10.1007%2F978-981-10-8240-5_30