Posting Frequency
[1] Total posts ()
The total number of tweets during using the hashtag #JORWebDesign6406
[2] Total number of posts each hour
The total number of tweets each hour using the hashtag #JORWebDesign6406
[3] All tweets
Content Descriptions
[4] Content types ()
Users can post various content, including hashtags, urls, user handles, multi-media or regular text. The height of the graph represents the whole number of tweets for a particular day. Each coloured segment or stack represents a category (in this case the number of tweets with urls, hashtags, media, or user handles) that make up parts of the whole.
[5] Engagement type ()
Twitter users can create an original post, or retweet or reply to an existing tweet. The height of each bar represents the whole number of tweets for a particular day. Each coloured segment or stack represents a category (in this case the number of tweets that are replies, retweets, or original) that make up parts of the whole.

Sentiment describes the emotionality of the tweet, and whether it is positive, negative or neutral.
We use the NLTK SentimentAnalyzer , a Python package to implement and facilitate Sentiment Analysis tasks using NLTK features and classifiers.

Subjectivity is the degree of factuality or opinionated language in tweets. To calculate the subjectivity of sentences, we use the NLTK subjectivity classifier, trained on the Subjectivity Dataset (Pang & Lee, 2004).
[8] Top 10 Hashtags ()
Hashtags contained in the tweet (excluding #JORWebDesign6406)


[9] Top 10 named entities ()
Named entities are proper names of persons, places, or organizations


User Information
[10] Top 10 contributors ()

Twitter users who post, retweet or reply most frequently
[11] Top 10 most negative ()
See examples of tweets by sentiment type
Users with most negative sentiment
[12] Top 10 most positive ()
See examples of tweets by sentiment type
Users with most positive sentiment
[13] Top 10 most mentioned ()

Users most mentioned