Youtube烹饪频道观众在Hinglish数据集中的评论

Data Set Information: The datasets are taken from top 2 Indian cooking channel named Nisha Madhulika channel and Kabitaa€?s Kitchen channel. Both the datasets are divided into seven categories :- Label 1- Gratitude Label 2- about the recipe Label 3- about the video Label 4- Praising Label 5- Hybrid Label 6- Undefined Label 7- Suggestions and queries All the labelling has been done manually. Nisha Madhulika dataset: Dataset characteristics: Multivariate Number of instances: 4900 Area: Cooking Attribute characteristics: Real Number of attributes: 3 Date donated: March, 2019 Associate tasks: Classification Missing values: Null Number of subscribers: 7,063,604 Kabita Kitchen dataset: Dataset characteristics: Multivariate Number of instances: 4900 Area: Cooking Attribute characteristics: Real Number of attributes: 3 Date donated: March, 2019 Associate tasks: Classification Missing values: Null Number of subscribers: 4,867,502 There are two separate datasets file of each channel. The files with preprocessing names are generated after doing the preprocessing and exploratory data analysis on both the datasets. This file includes: a€￠ Id a€￠ Comment text a€￠ Labels a€￠ Count of stop-words a€￠ Uppercase words a€￠ Hashtags a€￠ Word count a€￠ Char count a€￠ Average words a€￠ Numeric The main file includes: a€￠ Id a€￠ comment text a€￠ Labels Attribute Information: Provide information about each attribute in your data set. Relevant Papers: Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach [Web link] Citation Request: If you are using the data. Please cite the paper. Bibtex reference @Article{bdcc3030037, AUTHOR = {Kaur, Gagandeep and Kaushik, Abhishek and Sharma, Shubham}, TITLE = {Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach}, JOURNAL = {Big Data and Cognitive Computing}, VOLUME = {3}, YEAR = {2019}, NUMBER = {3}, ARTICLE-NUMBER = {37}, URL = {[Web link]}, ISSN = {2504-2289}, ABSTRACT = {The success of Youtube has attracted a lot of users, which results in an increase of the number of comments present on Youtube channels. By analyzing those comments we could provide insight to the Youtubers that would help them to deliver better quality. Youtube is very popular in India. A majority of the population in India speak and write a mixture of two languages known as Hinglish for casual communication on social media. Our study focuses on the sentiment analysis of Hinglish comments on cookery channels. The unsupervised learning technique DBSCAN was employed in our work to find the different patterns in the comments data. We have modelled and evaluated both parametric and non-parametric learning algorithms. Logistic regression with the term frequency vectorizer gave 74.01% accuracy in Nisha Madulika’s dataset and 75.37% accuracy in Kabita’s Kitchen dataset. Each classifier is statistically tested in our study.}, DOI = {10.3390/bdcc3030037} } MDPI and ACS Style Kaur, G.; Kaushik, A.; Sharma, S. Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach. Big Data Cogn. Comput. 2019, 3, 37.