What Do I Do? Type a keyword into the input field, then click the Query button. Recent tweets that contain your keyword are pulled from Twitter and visualized in the Sentiment tab as circles. Hover your mouse over a tweet or click on it to see its text. Words highlighted in bold blue italics or bold orange italics are the words being used to estimate the sentiment of a tweet. Blue words are evaluated as-is. Orange words are evaluated as though they are negated, for example, "happy" versus "not happy".
Twitter Sentiment Analysis Firefox Addon to See Sentiment of all Tweets
What Am I Seeing? Tweets are visualized in different ways in each of the tabs at the top of the window. Sentiment. Each tweet is shown as a circle positioned by sentiment, an estimate of the emotion contained in the tweet's text. Unpleasant tweets are drawn as blue circles on the left, and pleasant tweets as green circles on the right. Sedate tweets are drawn as darker circles on the bottom, and active tweets as brighter circles on the top. Hover your mouse over a tweet or click on it to see its text. Topics. Tweets about a common topic are grouped into topic clusters. Keywords above a cluster indicate its topic. Tweets that do not belong to a topic are visualized as singletons on the right. Hover your mouse over a tweet or click on it to see its text. Heatmap. Pleasure and arousal are used to divide sentiment into a 88 grid. The number of tweets that lie within each grid cell are counted and used to color the cell: red for more tweets than average, and blue for fewer tweets than average. White cells contain no tweets. Hover your mouse over a cell to see its tweet count. Tag Cloud. Common words from the emotional regions Upset, Happy, Relaxed, and Unhappy are shown. Words that are more frequent are larger. Hover the mouse over a word to see how often it occurred. Timeline. Tweets are drawn in a bar chart to show the number of tweets posted at different times. Pleasant tweets are shown in green on the top of the chart, and unpleasant tweets are shown in blue on the bottom. Hover the mouse over a bar to see how many tweets were posted at the given time. Map. Tweets are drawn on a map of the world at the location where they were posted. Please note most Twitter users do not provide their location, so only a few tweets will be shown on the map. Hover your mouse over a tweet or click on it to see its text. Affinity. Frequent tweets, people, hashtags, and URLs are drawn in a graph to show important actors in the tweet set, and any relationship or affinity they have to one another. Hover your mouse over a node, or click on a node to see its tweets. Narrative. Selecting a anchor tweet of interest from the tweet list displays a time-ordered sequence of tweets that form conversations or narrative threads passing through the anchor tweet. Hover your mouse over a node or click on it to see its text. Hover your mouse over a link to see all threads that pass through the link, or click on it to see the tweets in each thread. Tweets. Tweets are listed to show their date, author, pleasure, arousal, and text. You can click on a column's header to sort by that column.
AI-Driven Social Media Dashboard deploys an Amazon Elastic Compute Cloud (Amazon EC2) instance running in an Amazon Virtual Private Cloud (Amazon VPC) that ingests tweets from Twitter. An Amazon Kinesis Data Firehose delivery stream loads the streaming tweets into the raw prefix in the solution's Amazon Simple Storage Service (Amazon S3) bucket. Amazon S3 invokes an AWS Lambda function to analyze the raw tweets using Amazon Translate to translate non-English tweets into English, and Amazon Comprehend to use natural-language-processing (NLP) to perform entity extraction and sentiment analysis.
A second Kinesis Data Firehose delivery stream loads the translated tweets and sentiment values into the sentiment prefix in the Amazon S3 bucket. A third delivery stream loads entities in the entities prefix using in the Amazon S3 bucket.
However you feel about it, recent events regarding the US operation in Pakistan to neutralise OBL have been all over the news. I ran the day of the announcement through my Twitter sentiment analysis method in an effort to see how people reacted in the UK.
Besides, you get all kinds of other analytics: growth analysis, competitor analysis, sentiment analysis. You know how much people talk about your brand, where that happens, who the people that mention your brand are, and what they say. 2ff7e9595c
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