| Title |
SentiHealth: creating health-related sentiment lexicon using hybrid approach
|
|---|---|
| Published in |
SpringerPlus, July 2016
|
| DOI | 10.1186/s40064-016-2809-x |
| Pubmed ID | |
| Authors |
Muhammad Zubair Asghar, Shakeel Ahmad, Maria Qasim, Syeda Rabail Zahra, Fazal Masud Kundi |
| Abstract |
The exponential increase in the health-related online reviews has played a pivotal role in the development of sentiment analysis systems for extracting and analyzing user-generated health reviews about a drug or medication. The existing general purpose opinion lexicons, such as SentiWordNet has a limited coverage of health-related terms, creating problems for the development of health-based sentiment analysis applications. In this work, we present a hybrid approach to create health-related domain specific lexicon for the efficient classification and scoring of health-related users' sentiments. The proposed approach is based on the bootstrapping modal, a dataset of health reviews, and corpus-based sentiment detection and scoring. In each of the iteration, vocabulary of the lexicon is updated automatically from an initial seed cache, irrelevant words are filtered, words are declared as medical or non-medical entries, and finally sentiment class and score is assigned to each of the word. The results obtained demonstrate the efficacy of the proposed technique. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Pakistan | 3 | 38% |
| Unknown | 5 | 63% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 88% |
| Scientists | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 90 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 22 | 24% |
| Student > Master | 13 | 14% |
| Lecturer | 6 | 7% |
| Student > Doctoral Student | 5 | 6% |
| Researcher | 5 | 6% |
| Other | 17 | 19% |
| Unknown | 22 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 37 | 41% |
| Business, Management and Accounting | 3 | 3% |
| Engineering | 3 | 3% |
| Decision Sciences | 2 | 2% |
| Social Sciences | 2 | 2% |
| Other | 11 | 12% |
| Unknown | 32 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 September 2017.
All research outputs
#4,413,765
of 23,979,951 outputs
Outputs from SpringerPlus
#260
of 1,858 outputs
Outputs of similar age
#78,025
of 369,559 outputs
Outputs of similar age from SpringerPlus
#42
of 247 outputs
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