metadata
license: cc-by-nc-sa-4.0
tags:
- Sentiment Analysis
configs:
- config_name: default
data_files:
- split: 1star
path: SINAI-SA-corpus/1/*.txt
- split: 2star
path: SINAI-SA-corpus/2/*.txt
- split: 3star
path: SINAI-SA-corpus/3/*.txt
- split: 4star
path: SINAI-SA-corpus/4/*.txt
- split: 5star
path: SINAI-SA-corpus/5/*.txt
Dataset Description
Paper: Experiments with SVM to classify opinions in different domains
Point of Contact: [email protected]
This corpus has been prepared by the SINAI group in December 2008. SINAI SA (Sentiment Analysis) was created by tracking the Amazon website. Nearly 2,000 comments were extracted from different cameras.
Structure: The SINAI corpus contains 5 directories and each represents the number of stars for reviews. (eg directory 1 contains rated with a star). Each directory contains a file in plain text by document/comment.
The amount of comments is as follows:
- 1…star: 78 comments
- 2…stars: 67 comments
- 3…stars: 97 comments
- 4…stars: 411 comments
- 5…stars: 1,290 comments Total: 1,943 comments
| Camera | Comments |
|---|---|
| CanonA590IS | 400 |
| CanonA630 | 300 |
| CanonSD1100IS | 426 |
| KodakCx7430 | 64 |
| KodakV1003 | 95 |
| KodakZ740 | 155 |
| Nikon5700 | 119 |
| Olympus1030SW | 168 |
| PentaxK10D | 126 |
| PentaxK200D | 90 |
| Total | 1,943 |
Licensing Information
SINAI-SA Corpus is released under the Apache-2.0 License.
Citation Information
@article{RUSHDISALEH201114799,
title = {Experiments with SVM to classify opinions in different domains},
journal = {Expert Systems with Applications},
volume = {38},
number = {12},
pages = {14799-14804},
year = {2011},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2011.05.070},
url = {https://www.sciencedirect.com/science/article/pii/S0957417411008542},
author = {M. {Rushdi Saleh} and M.T. Martín-Valdivia and A. Montejo-Ráez and L.A. Ureña-López},
keywords = {Opinion mining, Machine learning, SVM, Corpora},
abstract = {Recently, opinion mining is receiving more attention due to the abundance of forums, blogs, e-commerce web sites, news reports and additional web sources where people tend to express their opinions. Opinion mining is the task of identifying whether the opinion expressed in a document is positive or negative about a given topic. In this paper we explore this new research area applying Support Vector Machines (SVM) for testing different domains of data sets and using several weighting schemes. We have accomplished experiments with different features on three corpora. Two of them have already been used in several works. The last one has been built from Amazon.com specifically for this paper in order to prove the feasibility of the SVM for different domains.}
}