Unveiling X/Twitter's Sentiment Landscape: A Python Crawler That Maps Opinion Using Advanced Search
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Abstract
Sentiment analysis is a method for determining attitudes towards a particular event or topic. Twitter or widely known as X now is a popular micro-blogging social media platform frequently used to express emotions. It is well-suited for sentiment analysis. However, some Twitter data retrieval applications have limited search capabilities. Sometimes, Twitter searches can lead to discussions that are unrelated to the intended topic, such as scams or frauds that exploit popular or common hashtags. In addressing this issue Twitter offers an advanced search function that enables detailed topic searches to meet specific information needs, such as sentiment analysis. This study presents a Python-based application for crawling Twitter data using a detailed search similar to advanced search on Twitter. The processed data is used to determine the average sentiment value of the searched topics. To calculate this sentiment value, the researcher utilises Stanza during the search process.
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