Sentiment Analysis on WhatsApp Group Chat Using R
In today’s world, the most popular chat application for fast communication is WhatsApp. Every smart phone user uses this mobile application for message communication. It is free and very fast communication mobile application, but nowadays people have beco
- PDF / 1,018,191 Bytes
- 9 Pages / 439.37 x 666.142 pts Page_size
- 88 Downloads / 285 Views
1 Introduction In today’s internet era, the fastest communication through mobile application is WhatsApp, in which every smart phone user is sharing their thought, sentiment, and opinion with each other [1]. The main feature of this application is the group chat, through which a message can be easily accessed to many people, and that is why nowadays any news is spread by WhatsApp very quickly whether the news is constructive or unconstructive. Therefore, it has become very important to analyze each group’s chat and find out how positive the group is. WhatsApp provides a facility to convert any group chat into a text file via e-mail but it is quite difficult task to analyze it manually. Sentimental analysis is a process by which the statement can be analyzed from large text and this process is also known as opinion mining [2]. R programming lab is a programming language that has many functions and packages available for text preprocessing, mining, and sentiment study. We can easily execute text mining and sentiment investigation by R Studio [3]. In this research paper, I reviewed the work done in the field of sentimental analysis research and has also reviewed the search work done on the data of WhatsApp. I have retrieved WhatsApp group data of all members of an organization and applied sentiment study and text mining with the help of R Studio. I have also analyzed the results of the level of emotions and sentiment of this WhatsApp group. The rest of the paper is organized as follows: Sect. 2 discusses brief journalism of sentiment investigation and WhatsApp chat analysis. Section 3 focuses on implementation of sentiment investigation in R. Section 4 discusses about the results obtained for sentiment analysis. Finally, Sect. 5 concludes the research paper.
S. Joshi (B) Department of Computer Applications, Samrat Ashok Technological Institute, Vidisha, MP, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 R. K. Shukla et al. (eds.), Data, Engineering and Applications, https://doi.org/10.1007/978-981-13-6347-4_5
47
48
S. Joshi
2 Literature Review Sentiment analysis or it is also known as opinion mining is a very trendy research topic in the field of big data analytics and data mining [4–7]. Lot of research works are done to analyze sentiment and opinion from various real-time big data like social networking site, micro-blogging site, mobile messaging applications, product review, movie review data, and many more [8–17]. As per [4], sentiment examination is a very critical task and it can be performed by five main steps which is presented in Fig. 1.1. The main categories of sentiment classification approaches are first machine learning, second lexicon based, and third hybrid approach [4]. There are various tools available like Emoticons, SentiWordNet, etc. for sentiment analysis [4]. One author performed experiment on 1000 posts from Facebook data and applied sentiment mining system [11]. WhatsApp provides a good solution for the problem of communication between two individuals as we
Data Loading...
