This study focuses on sentiment analysis of messages posted on a medical forum, categorizing them into five types: encouragement, gratitude, confusion, facts, and facts + sentiments. The analysis involves manual sentiment annotation, the use of affective lexicons, and machine learning classification. Empirical results from 752 posts about infertility treatments show significant improvements in multi-class sentiment classification of online messages.
-
Sokolova, Marina, and Victoria Bobicev. "What sentiments can be found in medical forums?." RANLP, 2013.
-
Members
- IC Essentials
- eivindsimensen
- abobader
- Kirill Gromov
- V0RT3X
- JoelR
- envy
- Analog
- Sinistra
- DawPi
- Dilip
- Square Wheels
- N700
- Majster87
- Adriano Faria
- opentype
- adik
- bernhara
- Como
- GrantHorizons
- A Zayed
- terabyte
- Videoflicks
- Voyage
- burnyourfeelings
- onlyME
- Jon Erickson
- Chris Anderson
- aXenDev
- AnonDoggo
- Empire
- Nathan Explosion
- Labis
- XwReK
- COSMIN
- Hexzon
- IPS THEME
- Venthas
- TomCat
- ReyDev
- StevenM
- GazzaGarratt
- Live Games
- Charlie Feigel
- Auto Evoke
- Steph40
- JoeyM
- Ryan
- master963
- John Horton
- TwinTurbo
- Uncrowned Gaurd
- Foxtrek64
- Claudia999
- bing11
- Andy Y
- Copycat
- Karyexo Karyexooo
- Kelkrel
- Myr
- dolphin
- ali hagi
- Richard Arch
- yaotzin
- lanc3lot
- Brian
- Nicolas PC
- Synergy
- Maria
- Nomad
- TheLlamaman
- scaz
- The SoftBay
- TracyIsland
- Yurii
- william trowbridge
- Cristian Croitor
- Asare
- djdan36
- shahed
- YalcinA
- VAHID
- send2yoni
- ZLTRGO
- Paul Kaiser
- Paul
- Omar Barbeytia carretero
- Ryancoolround
- rainx
- YourSharona
- Kentraiyle Robinson
- MichaelR
- Edward Ellas
- PrettyPixels
- Denis Dyack
- DursunKaptan
- MissB
- aLEX49566
- Codepixel
- alsl sndnxnx