Implication of Machine Learning Techniques on Enhancing Social Media Marketing

Abstract

Study 1: Social media plays a crucial role for marketers, business promoters, and consumers. Understanding buyer behaviour and preferences is essential for predicting purchasing decisions. This will assist the marketers to maintain and increase the customer base and also to increase the revenue inflow. This is possible only if the organization understands the buying decisions of the consumer and other data pertaining to the purchase intentions and desire. Data mining techniques are going to be a very supportive tool in the extracting these data. This paper examines social media data analytics using machine learning tools, using the Waikato Environment for Knowledge Analysis (WEKA). WEKA outperforms other algorithms in precision, recall, and F-measure parameters. WEKA is found to surpass its competitors of interest, when it is compared. newlineStudy 2: Social media acts as one of the eminent platforms for communication. Twitter is one of the leading social media microblogging platforms, where users can post and interact. #Hashtags specify the tweeter trends on a certain topic. Currently, the hashtag value or trend ranking for a particular hashtag has been calculated based on the cumulative number of tweets. This type of cumulative amount of hashtag ranking may result in an anonymous intervention of irrelevant tweets, which affects social media marketing. The proposed approach uses the relevance of tweets and #hashtags to improve and identify the suspicious or irrelevant tweets of media marketing. The proposed research work uses the linear regression algorithm, which is one of the familiar machine learning approaches to explain the spam tweet generation and the newlineiv newlinemethod to identify. The test results found the proposed system has 84% of significance when compared to the market analysis algorithms. newline