Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms

Aim This study aims to gain insights into the trending yahoo-yahoo topic on Twitter using content analysis of selected historical tweets. Methodology The widgets and workflow engine in the Orange Data mining toolbox were employed for all the text mining tasks. 5500 tweets were collected from Twitter using the “yahoo yahoo” hashtag. The corpus was pre-processed using a pre-trained tweet tokenizer, Valence Aware Dictionary for Sentiment Reasoning (VADER) was used for the sentiment and opinion mining, Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) was used for topic modelling.

In contrast, Multidimensional scaling (MDS) was used to visualize the modelled topics. Results Results showed that "yahoo" appeared in the corpus 9555 times, 175 unique tweets were returned after duplicate removal. Contrary to expectation, Spain had the highest number of participants tweeting on the 'yahoo yahoo' topic within the period.

The result of Vader sentiment analysis returned 35.85%, 24.53%, 15.09%, and 24.53%, negative, neutral, no-zone, and positive sentiment tweets, respectively. The word yahoo was highly representative of the LDA topics 1, 3, 4, 6, and LSI topic 1. Conclusion It can be concluded that emojis are even more representative of the sentiments in tweets faster than the textual contents.

Also, despite popular belief, a significant number of youths regard cybercrime as a detriment to society.