mirror of
https://github.com/Lissy93/twitter-sentiment-visualisation.git
synced 2021-05-12 19:52:18 +03:00
comparison of dffernt sa methods jade
This commit is contained in:
Alicia Sykes
@@ -1,96 +1,95 @@
|
||||
extends layout
|
||||
block content
|
||||
main.content.whitebg.maxWidth75
|
||||
h2 Comparison of methods of calculating Sentiment Analysis
|
||||
p
|
||||
| There are a number of different methods of calculating
|
||||
| sentiment analysis (SA). For this experiment I compare the
|
||||
| two most common methods, dictionary-based SA and natural
|
||||
| language understanding SA.
|
||||
| A set of Tweets will be fetched from the Twitter API, and
|
||||
| a script will calculate the sentiment for with both methods
|
||||
| and display the results in the table and charts below.
|
||||
main.content.maxWidth75
|
||||
div.whitebg.card-padding.curved
|
||||
h2 Comparison of methods of calculating Sentiment Analysis
|
||||
p
|
||||
| There are a number of different methods of calculating
|
||||
| sentiment analysis (SA). For this experiment I compare the
|
||||
| two most common methods, dictionary-based SA and natural
|
||||
| language understanding SA.
|
||||
| A set of Tweets will be fetched from the Twitter API, and
|
||||
| a script will calculate the sentiment for with both methods
|
||||
| and display the results in the table and charts below.
|
||||
|
||||
div.row
|
||||
include ./component_divider
|
||||
div.col.s12.m6
|
||||
h3 Dictionary-based SA
|
||||
p
|
||||
| Dictionary-based sentiment analysis uses predefined dataset
|
||||
| of words annotated with their semantic values. The algorithm
|
||||
| simply looks up the semantic value for each word and then
|
||||
| calculates the overall sentiment for the sentence based on
|
||||
| the values for each word. <br />
|
||||
| The word list used in this example is the AFINN-111.
|
||||
| You can view the sourcecode and documentation for the
|
||||
| dictionary-based SA module
|
||||
| <a href="https://github.com/Lissy93/sentiment-analysis">here</a>
|
||||
|
||||
div.col.s12.m6
|
||||
h3 Natural Language Understanding SA
|
||||
p
|
||||
| Natural language understanding-based sentiment analysis is a
|
||||
| little more complex, and takes longer for each calculation. It
|
||||
| also tends to be more costly as well, however the results are
|
||||
| considerably more accurate. It is also possible to extract
|
||||
| more than just a positivity value for each string, you can
|
||||
| drill down into much more detail about the attitudes conveyed.
|
||||
| <br />
|
||||
| For this example the HP Haven OnDemand API has been used
|
||||
|
||||
include ./component_divider
|
||||
|
||||
div.col.s12.m6(style='float:right')
|
||||
h3 Data Sauce
|
||||
|
||||
if(searchTerm)
|
||||
p Showing results related to #{searchTerm}
|
||||
|
||||
else
|
||||
div.row
|
||||
include ./component_divider
|
||||
div.col.s12.m6
|
||||
h3 Dictionary-based SA
|
||||
p
|
||||
| The following examples are using Twitter data relating to the
|
||||
| Edward Snowdon news story in 2013. There is also a third
|
||||
| column added which represents the sentiment calculated by a
|
||||
| number of humans in a questionnaire, this is to act as a
|
||||
| benchmark <br>
|
||||
| You can enter your own search query below, Twitter data will
|
||||
| be fetched and the sentiment of each Tweet then calculated.
|
||||
| Dictionary-based sentiment analysis uses predefined dataset
|
||||
| of words annotated with their semantic values. The algorithm
|
||||
| simply looks up the semantic value for each word and then
|
||||
| calculates the overall sentiment for the sentence based on
|
||||
| the values for each word. <br />
|
||||
| The word list used in this example is the AFINN-111.
|
||||
| You can view the sourcecode and documentation for the
|
||||
| dictionary-based SA module
|
||||
| <a href="https://github.com/Lissy93/sentiment-analysis">here</a>
|
||||
|
||||
div.col.s12.m6
|
||||
h3 Natural Language Understanding SA
|
||||
p
|
||||
| Natural language understanding-based sentiment analysis is a
|
||||
| little more complex, and takes longer for each calculation. It
|
||||
| also tends to be more costly as well, however the results are
|
||||
| considerably more accurate. It is also possible to extract
|
||||
| more than just a positivity value for each string, you can
|
||||
| drill down into much more detail about the attitudes conveyed.
|
||||
| <br />
|
||||
| For this example the HP Haven OnDemand API has been used
|
||||
|
||||
include ./component_divider
|
||||
|
||||
div.col.s12.m6(style='float:right')
|
||||
h3 Data Sauce
|
||||
|
||||
if(searchTerm)
|
||||
p Showing results related to #{searchTerm}
|
||||
|
||||
else
|
||||
p
|
||||
| The following examples are using Twitter data relating to the
|
||||
| Edward Snowdon news story in 2013. There is also a third
|
||||
| column added which represents the sentiment calculated by a
|
||||
| number of humans in a questionnaire, this is to act as a
|
||||
| benchmark <br>
|
||||
| You can enter your own search query below, Twitter data will
|
||||
| be fetched and the sentiment of each Tweet then calculated.
|
||||
|
||||
div.col.s12.m6
|
||||
h3 Custom Data Sauce
|
||||
input#txtKeyword(
|
||||
placeholder='Enter keyword to calculate custom sentiment comparison data',
|
||||
type='text', class='validate', value="#{searchTerm}")
|
||||
a#btnCalculate.waves-effect.waves-light.btn(href='', style='float:right', onclick='showLoader()') Calculate
|
||||
|
||||
include ./component_divider
|
||||
|
||||
div.row
|
||||
div.col.s12.m6
|
||||
div.whitebg.card-padding.curved.card-margin(style='width:35em')
|
||||
span#summarry_bar_ch
|
||||
|
||||
div.col.s12.m6
|
||||
h3 Custom Data Sauce
|
||||
input#txtKeyword(
|
||||
placeholder='Enter keyword to calculate custom sentiment comparison data',
|
||||
type='text', class='validate', value="#{searchTerm}")
|
||||
a#btnCalculate.waves-effect.waves-light.btn(href='', style='float:right') Calculate
|
||||
|
||||
include ./component_divider
|
||||
|
||||
div.col.s12.m6
|
||||
br
|
||||
span#summarry_bar_ch
|
||||
|
||||
div.col.s12.m6
|
||||
img(src='/images/sentiment-comparison.png', width='500px')
|
||||
img(src='/images/sentiment-comparison.png', width='500px').whitebg.card-padding.curved
|
||||
|
||||
|
||||
include ./component_divider
|
||||
div.row.whitebg.card-padding.curved
|
||||
span#scatter_ch
|
||||
|
||||
div.row.whitebg.card-padding.curved
|
||||
table#table_ch.bordered.centered.responsive-table(style='width:71em')
|
||||
|
||||
div.row.whitebg.card-padding.curved
|
||||
span#bar_ch
|
||||
|
||||
|
||||
h3 Scatter Chart of Sentiment Data
|
||||
span#scatter_ch
|
||||
|
||||
h3 Raw Data
|
||||
span#table_ch
|
||||
|
||||
h3 Bar Chart
|
||||
span#bar_ch
|
||||
|
||||
|
||||
script(type='text/javascript', src='https://www.google.com/jsapi')
|
||||
script(type='text/javascript').
|
||||
var sentimentResults = !{JSON.stringify(results)};
|
||||
script(type = 'text/javascript', src = '/bower_components/d3/d3.min.js')
|
||||
script(type='text/javascript', src='/javascripts/charts/comparison.js')
|
||||
script(type='text/javascript', src='https://www.google.com/jsapi')
|
||||
script(type='text/javascript').
|
||||
var sentimentResults = !{JSON.stringify(results)};
|
||||
script(type = 'text/javascript', src = '/bower_components/d3/d3.min.js')
|
||||
script(type='text/javascript', src='/javascripts/charts/comparison.js')
|
||||
|
||||
include ./footer
|
||||
|
||||
|
||||
Reference in New Issue
Block a user