Abstract
Today users on the social platform are expressing their emotions, ideas, proposals and views. The opinion may articulate critical opinions in various ways and may include different polarities such as positive, negative or neutral and it is often a difficult challenge for people to appreciate the feeling of each opinion and the time it takes. The analysis of the feeling in each statement will resolve this issue. This paper presents a framework to analyze social media comments e.g. Facebook and identify a category of friends. First, some public profiles are selected and comments are retrieved from different posts of them. Secondly, those comments are stored as dataset and pre-processed for sentiment analysis. After that, the pre-processed data trained and tested in a sentiment analysis model developed by us. From the sentiment of data, we then identify friends. For example, a friend with a positive sentiment of comment can be considered as a good friend. The evaluation of the performance is measured. A decent accuracy is achieved by the system.
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Hyder, T., Karim, R., Arefin, M.S. (2022). Developing a System to Analyze Comments of Social Media and Identify Friends Category. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing & Optimization. ICO 2021. Lecture Notes in Networks and Systems, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-030-93247-3_74
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DOI: https://doi.org/10.1007/978-3-030-93247-3_74
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