MULTI-DIMENSIONAL SENTIMENT VECTOR from Amazon HMD VR device review.
- Citation Author(s):
- Submitted by:
- Yunho Maeng
- Last updated:
- Tue, 04/04/2023 - 05:00
- Data Format:
This dataset was created by following these steps. First, online reviews of HMD VR devices are collected and refined. Second, variables are deduced from previous studies, and then appropriate keyword candidates for the deduced variables are selected. Topic modeling is conducted to examine whether the deduced variables sufficiently represent all the reviews, and other variables are added if necessary. Third, an in-depth interview is conducted through a survey to examine whether the selected variables and keywords are properly reflected in the reviews and whether any inappropriate items are removed. Fourth, the Natural Language Toolkit (NLTK), an open-source library, is used to deduce the sentiment score of each measured item in a sentence unit. Fifth, the multi-dimensional sentiment vector (MDSV) of the reviews is created based on the sentiment scores deduced in a sentence unit. Sixth, the review rating is predicted through regression using a deep neural network based on the MDSV as input data.
we make use of the uploaded dataset to execute the analysis and computations detailed in the Jupyter Notebook (.ipynb) file available at the following Code Ocean capsule: https://codeocean.com/capsule/9125707. The dataset serves as a crucial input for the code provided in this capsule, enabling us to obtain the desired results and insights as part of our research.
- amazon_hmdvr_df_data.csv (600.24 kB)
[Updated] Jupyter Notebook (.ipynb) file available at the following Code Ocean capsule: https://codeocean.com/capsule/5885125/tree/v1