WhatsApp Chat Sentiment Analysis in R

Introduction to Twitter Analytics


What is Twitter Analytics


The growing phenomena of social media like Facebook, Twitter, LinkedIn and Instagram with each one has its own characteristics and its usages are constantly affecting the society. Facebook, for example, is considered as a social network where everyone in the network has a reciprocated relationship with another one in the same network. The relationship, in this case, is undirected. Conversely, in Twitter, everyone in the network does not necessarily have a reciprocated relationship with others. In this case, the relationship is either directed or undirected.

There are different types of twitter data such as user profile data and tweet messages. The user data is considered static, while the tweets are dynamic. Tweets could be textual, images, videos, URL, or spam tweets.

Twitter-API is a widely used application to retrieve, read and write twitter data. GNU/GPL application like YourTwapperKeeper tool, which is a web-based application that stores social media data in MySQL tables. However, YourTwapperKeeper in storing and handling the large size of data exhibits some limitations in using, as MySQL and spreadsheets databases can only store a limited size of data.

Ranking twitter users, it is important to study the characteristics of Twitter by studying the network-topology (number of followers/ followed) for each user in the dataset. There are different types of user’s networks like a network of users within a specific event (hashtag), a network of users in a specific user’s account, and a network of users within a group in the network, that is, Twitter Lists. Lists are used to group sets of users into topical or other categories to better organize and filter incoming tweets.

Many techniques are employed in ranking analysis. Twitter users are ranked by identifying the number of followers by studying the PageRank, and by the retweet rate. We follow different methods on Twitter users by using the Twitter Lists to classify users into the Elite users (Celebrities, Media news, Politicians, Bloggers, and Organizations) and the Ordinary users.

Applications of Twitter Analytics:

1. To define your buyer personas: Applying for analysis on Twitter, we can know the interests of followers (customers) of particular domain based on the tweets what they post like marketing, entrepreneurship, advertising, SEO, etc…

2. To know when our community is online: Achieving maximum exposure to our content is what we need and to know that we must know how many people reaching our tweets, and when our Twitter community is online as per it adjust your tweeting schedule accordingly.

3. To determine your Twitter ads are worth the money: This helps you pinpoint which paid promotions are working and which ones aren't.

4. To analyse what content is trending: Through this, we can see the top interests of our followers based on what they tweet about. For example like tech news, technology, etc…

5. Sentimental analysis: Often it is not important to know what users are saying, but how they are saying it. “Sentiment analysis” seeks to automatically associate a piece of text with a “sentiment score”, a positive or negative emotional score. It states the behavior of followers.