Sentiment Analysis of Movies Based on Natural Language Processing
- DOI
- 10.2991/978-94-6463-172-2_130How to use a DOI?
- Keywords
- Natural Language Processing; Movie Emotions; Random Forests; Neural Networks
- Abstract
Movies are an important spiritual food for people to satisfy their spiritual needs in contemporary society. Each person’s evaluation of one movie is different. The data mining of movie-related data can make a certain accuracy prediction of the final rating of a movie. This paper shows a new movie rating prediction model is proposed by using nlp technology. It analyzes the data recorded in Movielens for nearly 45,000 movies. Some of the selected data include final rating, movie duration, title, budget, story overview, genre, and so on. This includes a large amount of textual data that needs to be converted into vector data using nlp techniques. In this paper, random forest and neural network are chosen as the main models and a lot of tuning is done to obtain a relatively accurate model, i.e., a random forest movie rating prediction model using natural language processing. This research can be used for upcoming or newly released movies that lack ratings, which is a good reference for audiences’ viewing choices and movie producers’ promotion and investment decisions.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Hanfei Zhu PY - 2023 DA - 2023/06/30 TI - Sentiment Analysis of Movies Based on Natural Language Processing BT - Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023) PB - Atlantis Press SP - 1232 EP - 1240 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-172-2_130 DO - 10.2991/978-94-6463-172-2_130 ID - Zhu2023 ER -