Abstract
Nowadays, Natural Language Processing has received the widespread attention from the natural sciences, and sentimental analysis is one of the most widely used NLP applications. In the age of big data, how to find the required information accurately and quickly has become the hotspot of current research. Based on the movie reviews of two movies from the same series, this paper studies the sentimental trend of movies reviews, in order to help the audience obtain a reference for movie choices. Term frequency-Inverse Document Frequency (TF-IDF) algorithm is applied to evaluate the importance of words in the reviews, and TextBlob sentiment analysis library of Python software is used to grade the sentiment scores of the two films. Finally, the sentiment score graph is drawn, which provides a strong support for the further identification of the movie characteristics of two films from the same series. What’s more, Support vector machines (SVM) model is utilized to do the classification of the movie reviews and achieved 85.2% accuracy.
