1. Finnish sentiment analysis and a tool for social media tracking
Helsinki

2. Who we are? Faculty of Business, ICT and Chemical Engineering
Program degree: Business information technology
Arto Kivinen
Tuomo Helo
Tuukka Ojanen

3. Why we are doing this? NEMO = Business Value from Negative Emotions
What kind of impact negative information does for business
Knowing emotional aspects of social media
Calculated positive, neutral and negative classification

4. Demonstration
Sentiment & Data analyzer

5. Sentiment analysis methods
Supervised classification analysis was performed by using multinomial logistic regression, support vector machine, and random forest algorithms
Twitter messages were classified into three categories: Negative sentiment, neutral, and positive sentiment
The training and test sets were based on more than 9000 manually classified tweets
Dictionaries of positive (>1500) and negative (>3000) Finnish words were used
The used features included word counts and percentages, but also e.g. link counts and user tag counts

6. Architecture BigData Social media tracker Third party software
MongoDB 3.2
MongoDB 3.2
Futusome
API
Aggregation
MapReduce
Crawler
Twitter/Facebook…
API
User
HTTP
Geocode enrichment
Desktop
WebSocket
NodeJS
Sentiment analysis
WebSocket
Drupal 8
PHP
HTTP
API
R script
Optimized
in threads
Mobile
Apache
HTTP
PostgreSQL

7. Partners & Use cases Futusome Tampere University of Technology
Super Analytics
Nooa Säästöpankki
Rud Pedersen
Skanska
S-TOK

8. Thank you! Questions? Arto Kivinen arto.kivinen@turkuamk.fi Tuomo Helo
Tuukka Ojanen