Analyzing Social Media Data Using Sentiment Mining and Bigram Analysis for the Recommendation of YouTube Videos

Abstract

In this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming, which has caused much alarm and controversy over recent years. Our intention is to recommend informative YouTube videos to those seeking a balanced viewpoint of this area and the key arguments/issues. To this end we analyze Twitter data; Reddit comments and posts; user comments, view statistics and likes/dislikes of YouTube videos. The combination of sentiment analysis with raw statistics and linking users with their posts gives deeper insights into their needs and quest for quality information. Sentiment analysis provides the insights into user likes and dislikes, graph theory provides the linkage patterns and relationships between users, posts, and sentiment.

Sentiment Analysis of Air Pollution on Social Media: Systematic Literature Review

“…Furthermore, when considering the years in which the research was conducted, the category of methods most frequently employed over the last two years is Neural Network/Deep Learning, found in 7 literature [8], [9], [10], [13], [18], [20], [22] followed by the Classic category present in 6 literature [8], [10], [11], [12], [13], [22]. The methods used in sentiment analysis found in the 15 literature is illustrated in Table 4.…”

“…Out of the 15 analyzed literature, 11 of them [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18] utilized the social media platform Twitter for sentiment analysis, 1 literature [19] used Weibo, which is a commonly used social media platform in China, and 2 literature [20], [21] used government websites. Furthermore, there is one literature [22] that utilized three social media platforms, namely Twitter, Reddit, and Youtube. The utilization of social media platforms in sentiment analysis is illustrated in Figure 2.…”

“…The data collection found in the 15 reviewed literatures can be categorized into three groups, namely data collection techniques, data source/format, and tools. In accordance with the mentioned categories, data collection used are: 1) Technique: Crawling Data by [20], [21]; 2) Data Source/Format: Twitter API by authors [8], [9], [11], [12], [14], [15], [18], Weibo API by authors [19], Tweepy by authors [16], [17], and Reddit API by authors [22]; 3) Tools: Python Library by authors [10], [16], SNScrape by authors [10], and R vosonSML package by authors [22]. Figure 3 displays the data collection alongside the corresponding research counts.…”

“…Data Preprocessing Technique [8], [10], [11], [14] Tokenization [8], [10], [11], [15], [20], [22] Stop-word removal [8], [10], [13]- [15], [22] Stemming [8] Feature extraction [9]- [13], [17]- [20], [22] Cleaning the punctuation, url, and emojis [10], [15] Case Folding [10] Bag of words [10] Labeling data…”