SOCIOPHYSICS APPLICATION FOR SENTIMENT ANALYSIS OF CINEMA MOVIES USING NAÃVE- BAYES AND TEXTBLOB WITH PARTICLE SWARM OPTIMIZATION
Movie industry has developed rapidly and there could be big risks of investing in this industry. Predictions are needed to determine how long the movie should be shown and how many cinemas will show it. In this final project, sentiment classification and polarity analysis are carried out to deter...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/83769 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Movie industry has developed rapidly and there could be big risks of investing in
this industry. Predictions are needed to determine how long the movie should be
shown and how many cinemas will show it. In this final project, sentiment
classification and polarity analysis are carried out to determine the performance of
a movie. In classifying the sentiment, two main methods are used: Naïve-Bayes
method with Particle Swarm Optimization (PSO) and TextBlob. The results shown
that the Naïve-Bayes method using Particle Swarm Optimization (PSO) could
increase the accuracy of movie review sentiment classification in the first 4 weeks
with an accuracy range of 80.70% to 84.99%. Predictions of movie performance
cannot be seen directly only from the sentiment score data of movie reviews in the
first 4 weeks. However, analysis using movie sentiment scores in the first 4 weeks
can describe the popularity and public sentiment of movies quite well.
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