Muhammad Aasim Qureshi1,*, Muhammad Asif1, Mohd Fadzil Hassan2, Ghulam Mustafa1, Muhammad Khurram Ehsan1, Aasim Ali1, Unaza Sajid1
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4987-5004, 2022, DOI:10.32604/cmc.2022.020544
Abstract In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53… More >
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