Analysis of evaluation tendency of Python film based on adaptive
SVM
Abstract
With the development of the economy and the improvement of people's living
standards, film, as an important part of enriching people's cultural life, can help people
enrich their leisure life through film, and at the same time, film can also achieve cultural
exchanges between different countries. In the nearly 100 years of film development,
people's understanding of film has gradually deepened, making film consumption an
indispensable part of the economy, Film evaluation plays a crucial role in film consumption
and development. Many audiences choose to watch films not only based on the theme and
cast, but also the evaluation of film and television works has become an important factor in
the choice of watching films. At the same time, the level of film evaluation can indirectly
reflect the audience's recognition of the film, and also provide certain data support for the
film producers and distributors, The film evaluation tendency will affect the box office of
the film to a certain extent. As the related technologies in the field of prediction technology
and statistical analysis become more mature, the content of film box office prediction and
film review analysis has become an important functional module of many film platforms.
Film evaluation and prediction will be affected by irregular data and lack of prediction
accuracy. Using existing computing technology to achieve more accurate film evaluation
and analysis can help better predict the box office performance of films.
Combining the current situation of the film market and the significance of film
evaluation and prediction, this paper uses Python programming language to develop and
design a system platform for film evaluation tendency analysis based on C/S structure. At
the same time, in order to ensure the accuracy of film evaluation and analysis data
information judgment, through adaptive SVM support vector machine algorithm
application, the classification and extraction of data information is realized, Combining the
content of film evaluation to achieve the classification of positive and negative film review
digital information, combining the statistical legend of film evaluation to help better
understand the tendency of film evaluation, combining data preprocessing, feature
quantification and model structure confirmation to achieve the analysis of film review,
which provides more detailed data information support for film box office prediction.
Keywords: Python language; MySQL database; Adaptive SVM; Tendency of film
evaluation;
