Abstract
In the modern era of technological development, the emergence of Web 2.0 applications, related to social media, the dissemination of opinions, feelings, and participation in discussions on various issues have become very easy, which have led to a boom in text mining and natural language processing research. YouTube is one of the most popular social sites for video sharing. This may contain different types of unwanted content such as violence, which is the cause of many social problems, especially among children like aggression and bullying at home, in school and in public places. The research work reports performance of two different sentiment lexicons when they were applied on video transcripts to detect violence in YouTube videos. The automation of process to detect violence in videos can be helpful for censor boards that can use the technology to restrict violent video for a certain age group or can fully block entire video regardless of age. The models were built using the existing sentiment lexicons. The dataset consists of 100 English video transcripts collected from the web and was annotated manually as violent and non-violent. Various experiments were performed on the dataset using English SentiWordNet (ESWN) and Vader Package with different text preprocessing settings. The Vader package outperformed the ESWN by providing 75% accuracy. ESWN results for all POS tagging with 66% accuracy were better than its result for adjectives POS tagging with 58% accuracy.
Highlights
In recent years, text mining has gained increasing attention as huge amounts of text data are created by using the web and social networks
To improve the classification accuracy, disambiguation method was used by calculating the sentiment score of the words by taking only the synsets of the word that corresponds with the part of speech tag “adjective”
The Sentiment Intensity Analyzer object was loaded from the VADER package, the polarity scores method was used to get the sentiment scores of the scenes [21]
Summary
Text mining has gained increasing attention as huge amounts of text data (unstructured data) are created by using the web and social networks. The increasing amount of text data has created a need for methods and algorithms which can be used to learn interesting patterns from the data in a scalable and dynamic way. Automated sentiment analysis (the computational study of people„s opinions and emotions about individuals, issues, events, topics and their attributes) can be used to analyze the text data and find interesting patterns and relationships about different topics. YouTube is a popular video sharing site, where users are allowed to upload, view, share, rate, and comment on videos, and subscribe to other YouTube users. It offers a wide variety of videos that have different contents including TV shows, video clips, documentary films, movie trailers, and educational videos, etc. Violence is the cause of many problems, especially among children like aggression and bullying at home, in school and in public places
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
