There that, the box office was predicted using Support

There has been a tremendous progress in the field of NaturalLanguage Processing especially in opinion mining which is another terminologyfor sentiment analysis. Every time there is an improvement in the field and newthings are discovered. Tirath et al 5 did a comparison between different machinelearning classifiers and found out that Random Forest produced the highestaccuracy with 88.95%. They designed an algorithm to classify the movie reviewsif it is positive, negative or neutral using sentiwordnet. Pallavi et al 6 did a comparison between different textclassifiers such as Wordnet, SentiWordNet and Opinion Lexicon and produced theresults for them. They designed a GUI where a movie review is given as inputand the output is classifying the movie review as positive, negative orneutral.

They also handled a negative word in a positive review by using acustom algorithm called as negation handling. In this all the words are checkedand if there is a negative word, then the polarity of the sentence ismultiplied with a ‘-‘.Synonyms were handled by combining words which has thesame meaning. For example movie, film, picture all mean the same and wheneverthese words it takes only the value ‘movie’. Nagamma et al 7 predicted the box office collection of themovie based on the online movie reviews.

Best services for writing your paper according to Trustpilot

Premium Partner
From $18.00 per page
4,8 / 5
4,80
Writers Experience
4,80
Delivery
4,90
Support
4,70
Price
Recommended Service
From $13.90 per page
4,6 / 5
4,70
Writers Experience
4,70
Delivery
4,60
Support
4,60
Price
From $20.00 per page
4,5 / 5
4,80
Writers Experience
4,50
Delivery
4,40
Support
4,10
Price
* All Partners were chosen among 50+ writing services by our Customer Satisfaction Team

They used TF-IDF (Term Frequency –Inverse Document Frequency) as their sentiment classifier. They used a formulafor calculating the polarity of a sentence that is to divide the number ofadjectives occurred in a document and the total adjectives in the document.Based on that, the box office was predicted using Support Vector Machine whichproduced an accuracy of 89%. Nagarjuna et al 8 used laptop reviews to find out if thelaptop is a good model or not. Each feature was extracted such as Screenresolution, processing speed, weight etc. They took care of Anaphora Resolutionwhich is one of the challenges faced in sentiment analysis.

They did this byusing Part of Speech tagging and use of SentiWordNet, finally using SVMclassifier to produce an accuracy of 88%. This is done even using Neural Network model 9 where theIMDB data is used as a dataset using keras. Keras is used the load the datasetin a neural network model format.

A one dimensional neural network model wasdesigned and produced an accuracy of 88.3%.