The Sentiment Analysis chart reveals the most positive (green) and negative (orange) tweets, as measured by the Natural Language Processing (NLP) algorithm. This algorithm is explained below. The vertical axis ranges from +2 (positive) to -2 (negative). The horizontal axis is made up of the dates of the tweets. Up to 50 of the most positive and negative tweets are visualized in the chart and listed in their respective date columns.
The pie chart in the top left part of the chart reveals the overall sentiment breakdown for the selected time period for all tweets. Hover over the two pie slices to get the percentages.
The bubbles in the chart represent the most significant individual tweets from the time period. The higher the bubble the more positive the sentiment, the lower the bubble the more negative. The size of the bubble indicates the level of engagement, where the larger the bubble the more retweets.
This chart includes the standard option to filter the results by stakeholder(s). Click on a stakeholder to select it and click again to deselect. Changing these stakeholder filters will also update the pie chart with the average sentiment breakdown of the remaining filtered tweets.
Click and drag your mouse to select a portion of the chart to drill down into a narrower time period. The pie chart and tweet lists below will update accordingly. Click on "Reset zoom" in the upper right of the chart to return to the original view.
Click a bubble and the corresponding tweet will be highlighted in the respective Positive or Negative Tweet columns below.
Below the bubble chart are two columns of tweets, positive tweets on the left and negative tweets on the right. Each tweet is scored using the NLP algorithm and the score is located below each tweet. Next to the score is the number of retweets.
Click on the Twitter handle to view the account details.
Click on the date of the tweet to view that specific tweet in Twitter.
Each tweet includes the standard options to view the history, filter the chart by that tweet, and add a custom tag to the tweet.
Sentiment Analysis is powered by a natural language processing (NLP) algorithm optimized for healthcare and is proprietary to Symplur. This algorithm extracts information from healthcare conversations in order to determine polarity about healthcare issues. It takes into account grammar analysis, sentence structure, parts of speech, punctuation, emoticons, slang terms, and shortened terms common in social media.
Each sentiment score is also weighed accordingly based on the tweet author's influence in healthcare.
The method used for determining sentiment employs a scaling system for three classes of neutral, positive and negative sentiment.
This sentiment algorithm is highly trainable. The advantage of training the algorithm to your particular context or specialty is that it results in even more accurate sentiment scoring. Training involves either (1) modifying the score attributed to an existing term, and/or (2) adding your own custom term(s) with your assigned score.
To adjust the score of an existing term, follow the steps below (see video below):
Click on the score you wish to edit.
Click on the Edit link of the term you want to modify in the table.
Enter the new score using the input field or adjust the slider to a new score.
Click on Save when finished. The system will automatically rerun the algorithm with your newly assigned score for the term.
To revert back to the default score of the term, click on Edit once again and then click on the trashcan icon. The system will automatically reset the score to its previous value and rerun the algorithm. Note that terms that are in the base dictionary of the sentiment algorithm cannot be deleted, only their scores can be modified.
To add a new term to be used by the sentiment algorithm follow the steps below (see video below):
In the Processed Text section, highlight the word(s) that you want to add.
Confirm your text selection by clicking on the Add button that appears below.
The new selected term will be added to the table where you can assign the corresponding score. (You must assign a score other than 0.)
Click on Save when finished. The system will automatically rerun the algorithm using the newly added term and score.
To delete the custom term click on Edit and click on the trashcan icon. The term will be removed from the table and the sentiment score will be recalculated. Note that because this is a custom term, and not part of the base dictionary, it will be deleted.
The icons in the upper right corner provide several additional options:
Table - The table icon changes the display to an Excel-like table. Click on the icon a second time to change the display back to the original format.
Rank By - Toggle between different ranking algorithms by algorithm name in the header. The available options are SymplurRank and Score.
Export Positive Tweets as CSV - Download the Positive Tweets (left column) into an Excel table file.
Export Negative Tweets as CSV - Download the Negative Tweets (right column) into an Excel table file.
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