Sentimental Analysis of Social Context Using Integration of PSO-Cuckoo Optimization and SVM Classifier
Praveetha Gobinathan, Shermin Shamsudden, Muhammad Arif · 2023
Social context is data such as user relationships and user experiences with information. Online commercial websites produce a significant amount of textual information every day through consumer reviews, comments, suggestions and tweets. Social media site comments have become a valuable resource for producers to evaluate public opinion about their goods. Firstly, any stop words and noise data such as links, the social media comments are deleted and pre-processed. In the emotion lexicon, the data is then matched and the second matching is achieved in the emoticon dictionary when the first matching succeeds. The emoticons are translated into vector form in the emoticon dictionary. The emotional features are vectored through this matching and other text features are considered. Lastly, for sentiment classification, the machine learning classification system is used. The results of the experiment indicate that emoticons have a strong impact on promoting the classification of social media posts by sentiment, and the suggested method of classification of sentiment obtained better than average results in terms of classification accuracy relative to state-of-the-art techniques.