Brand2c: An Analytical Tool For Brand Sentiment Analysis Based On Twitter Data
Sentiment analysis which can be also known as “emotion artificial intelligence” or “opinion mining”, refers to the process of determining whether a text contains positive, negative, or neutral emotions by assigning the weighted sentiment scores. Social media has now become an essential business solu...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40552/1/CB19067.pdf http://umpir.ump.edu.my/id/eprint/40552/ |
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| Summary: | Sentiment analysis which can be also known as “emotion artificial intelligence” or “opinion mining”, refers to the process of determining whether a text contains positive, negative, or neutral emotions by assigning the weighted sentiment scores. Social media has now become an essential business solution and a must for digital presence in today’s digitally connected world. Sentiment analysis is important for companies to learn about their customers’ needs and market trends. The main problem for the companies and the public user is lack of platform for company to know their brand reputation, company and public user does not have a platform to observe the overview of the customers’ opinion on the products from the social media, and company could not know how the customers feel about the competitors’ brands. A web-based analytical tool is developed for brand analysis on online social network which is Twitter to calculate the sentiment scores of the tweets. The web-based application consists of summarize data of the analysis, pie chart of the sentiment, graph for tweets per day, word cloud, table of the tweets with sentiment scores, graph for positive sentiment, graph for negative sentiment, graph for neutral sentiment, and report of the sentiment analysis. The selected development methodology that was used to develop the web-based analytical tool is Scrum as it is a flexible methodology. It enables teams to collaborate. To conclude, this project implements VADER to evaluate various linguistic and grammatical variations, in addition to assigning a score to words. By using VADER in this project, VADER’s capabilities range helps to evaluate a customers’ attitude based on the tweet to make predictions about market values. |
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