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Youtube烹饪频道观众在Hinglish数据集中的评论

Youtube烹饪频道观众在Hinglish数据集中的评论

Scene:

Computer

Data Type:

Classification
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Data Preview ? 647K

    Data Structure ?

    *数据结构实际以真实数据为准

    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.

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