Sentiment Analysis on Movie Reviews Using Twitter
| Autor: | Sajay Thomas Samuel, Booma Poolan Marikannan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
History
05 social sciences Sentiment analysis Advertising General Chemistry Condensed Matter Physics Computational Mathematics 0502 economics and business General Materials Science 0509 other social sciences Electrical and Electronic Engineering 050904 information & library sciences 050212 sport leisure & tourism Movie reviews |
| Zdroj: | Journal of Computational and Theoretical Nanoscience. 17:2869-2875 |
| ISSN: | 1546-1955 |
| DOI: | 10.1166/jctn.2020.9326 |
| Popis: | Machine learning can help people to perform complex tasks and solve problems as it uses historical data to learn its pattern and make predictions based on the past data. This research addresses the problem about movie reviews on social media specifically Twitter; where it will gather the tweets on movie reviews and display a rating based on the sentiment of the tweet. Twitter is an online social media website where people from all walks of life communicate by tweeting short updates without exceeding the character limit which is 240 characters. Twitter is continuously growing as a business and became one of the biggest platform for communication and instant messaging. Due to the large number of users, there are voluminous amounts of data available that can be used for more in depth information and insights and to get the sentiments from analysing the tweets. In today’s world, there are many applications that are using sentiment analysis in various fields such as to gets insights about a particular brand or product. To do sentiment analysis using the traditional ways can be time consuming and becomes very complex. The aim of this research is to investigate about the domain of sentiment analysis and incorporate a machine learning algorithm to create a system that is able to get and display the ratings of a particular movie. The machine learning algorithms used are Naïve Bayes Classifier and SVM. The algorithm with better accuracy will be chosen for the implementation phase. |
| Databáze: | OpenAIRE |
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