Temporal Exploration in 2D Visualization of Emotions on Twitter Stream

[...]we rather extend the borderline of some region spots for the sake of getting the dataset that covers all the regions of the country. While all tweets contain timestamps (i.e., at the moment when the tweet is posted), this data provides arbitrary information relevant to the present timeframe [18]. Besides we labelled appropriate attributes, creation date and tweet message, we also used tweet ID attribute related to the topic, which may contain useful information considering the use of retweet. Tweet ID played as a distinguisher role for every posted tweet regardless repeated tweet. [...]due to the need of text classification, we performed text operations such as parsing, stemming, stop word removal, and part-of-speech tagging by using third-party analysis called Porter Stemmer. [...]to get interactive visualization, we implement some animations into it based on the needs of temporal exploration and transitions as well, consist of: in the two-dimensional heat map, we enable element transition into each cell, which represented the number of data in color scale, with the duration to 2500 milliseconds using the duration() function; context focus via brushing provides zooming and panning in the small chart to work with the big chart one; zooming and panning are common and useful techniques, which work particularly well with Scalable Vector Graphics (SVG) based visualization; we develop three types of interactive stacked area chart visualization with the same animation. 3.