A Dynamic Model Of Social Media Monitoring Tools With Sentiment Analysis

ABSTRACT

The proposed system is a dynamic model of social media monitoring tool with sentiment

analysis. Social media platforms contain a lot of data which might be considered ambiguous

to businesses/organizations. Most organizations make use of social media platforms for

advertisement as they reach a large crowd in a short period of time and are also more

affordable compared to other options. Businesses find it difficult to have a clear vision/insight

on how well their business has grown after each advertisement or product release. The aim of

the project is to provide various relevant analysis including sentiment analysis on real-time

data from social media platforms for specified keywords or social media accounts defined by

registered users. Sentiment analysis is a process in which an algorithm is used to determine the

emotion of texts i.e. positive, negative or neutral. Sentiment analysis helps businesses

understand how their customers feel about their product. Businesses can also compare their

products with their competitors to have a clearer insight on how their services are doing online

compared to other businesses. This application was developed using the increment

methodology and successfully produced the desirable results for the first iteration of the

project. To accomplish this project Python language and PostgreSQL database are used. The

end-result of the first iteration is focused on Twitter data. It performs all the requirements

specified. At the end the application passed through different types of tests and the obtained

accuracy was 98%. The application will continue with future enhancements and the other

iterations.

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