Badriya Murdhi Alenzi, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Abdul Khader Jilani Saudagar*, Mohammed AlKhathami, Abdullah AlTameem
Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1311-1331, 2022, DOI:10.32604/iasc.2022.025861
- 03 May 2022
Abstract With the recent boom in the corpus size of sentiment analysis tasks, automatic annotation is poised to be a necessary alternative to manual annotation for generating ground truth dataset labels. This article aims to investigate and validate the performance of two widely used lexicon-based automatic annotation approaches, TextBlob and Valence Aware Dictionary and Sentiment Reasoner (VADER), by comparing them with manual annotation. The dataset of 5402 Arabic tweets was annotated manually, containing 3124 positive tweets, 1463 negative tweets, and 815 neutral tweets. The tweets were translated into English so that TextBlob and VADER could be More >
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