1. Sentiment analysis tools
IS/ICT
Jan Král, David Krénar, Roman Pospěch

2. What are SAT? Why use them?2
Analysis of the feelings (emotions, opinions, …)
Get information to the natural language (user comments, product reviews, …)
Read data and analyze the content:
manually – time and money, not comparable over time
SAT – less time, provide consistent, accurate and impartial results

3. Why use Facebook? 3 1.23 billion users per day (December 2016)
Own analysis tool – Insights
Track user demographics in numerical terms (the number of likes, shares, …)
Help map audience more interested posts
FB stores this only for 180 days (Heijmans, 2015)

4. Text Insights 4 SAT for the numerical and the textual data
Using public data + FB Graph API
Analyze given content create keyword or keyphrase for each topic

5. Example – „Hypertension Hub“5
Fig. 1: Tag cloud of weighted keyphrases based on full page content (left);
page metrics for the time interval 08/2013–11/2013 (right)
Source:

6. Twitter sentiment analysis tools6
~300 million active users
The main content of this social network are short posts called tweets
Purpose – tweet sentiment classification (positive, negative or neutral)

7. Benchmark analysis 7
Goal – accuracy of tweet classification in comparison with human judgment
15* stand-alone and 5 workbench tools
5 data sets which included tweets from 5 different topics

8. Stand-alone vs. Workbench tools8
Stand-alone tools
ready-to-use right after you open them
Workbench tools
require supervised learning-based model development on a labeled training set

9. Stand-alone tools benchmark results9
Tool
Average accuracy (%)
SentiStrength*
67.49
Chatterbox
67.43
Sentiment140*
66.46
Textalytics
66.22
Intridea
63.31
Table 1: Five best stand-alone tools sorted by highest average accuracy. Source: Abbasi et al., 2015.

10. Workbench tools benchmark results10
Tool
Average accuracy (%)
BPEF
71.38
Lightside
69.35
FRN
69.17
EWGA
68.12
RapidMiner
66.86
Table 1: Workbench tools sorted by highest average accuracy.
Source: Abbasi et al., 2015.

11. Source
Abbasi, A., Hassan, A., Dhar, M. (2015). Benchmarking Twitter Sentiment Analysis Tools. Retrieved February 27, 2017, from

12. 12 Google Cloud Platform Two solutions
Both independent on platform (Facebook, Twitter, web pages…)
Prediction API
Natural Language API

13. Prediction API Generic machine learning service
Can solve regression or classification problems
Sentiment analysis = classify text string with provided labels („sad“, „happy“…)
Steps
Collect data
Label collected data
Upload data to Google Cloud Storage
Train model
Make predictions based on trained model

14. Natural Language API Google’s sentiment analysis solution
Based on natural language processing
Only English is supported

15. Natural Language API - example
„My experience with Amazon was to BAD. I ordered JBL FLIP-3 PORTABLE WIRELESS SPEAKER on 25 March 17. And on 26 March I received the order but in the box I got 3 pieces of STONE instead of wireless speaker. And they said it was the mistake. Fake.“
Source:

17. Thank you for your attention.