Tweets sentiment analysis using multi-lexicon features and SMO Online publication date: Thu, 13-Jan-2022
by V.P. Lijo; Hari Seetha
International Journal of Embedded Systems (IJES), Vol. 14, No. 5, 2021
Abstract: Nowadays, people use social networks extensively to share their opinions about events and entities in personal life, politics and business. Twitter sentiment analysis aims at automatic identification and extraction of such opinionated data, gaining the attention of many researchers. Twitter gives a good opportunity to explore mass data, and it opens challenges of processing big data, and automatic identification and extraction of subjective information from short texts. Many methods and resources have been proposed in literature for automatic identification of subjective information from natural language texts. In this paper, we have proposed a fast mode of polarity detection using multi-lexicon features. The binary-valued multi-lexicon features save time and space for handling them for large data. Furthermore, we have used the modified-SMO with Apache Spark to process large datasets effectively. The experiments show that the method supports high scalability, and it improves the efficiency of the polarity detection.
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