Cricket Commentary Analysis
Text Classification and Natural Language Processing for Commentary Insights
By Huggingface Hub [source]
About this dataset
This dynamic cricket commentary dataset is a powerful tool for understanding and analyzing the game. With its three distinct datasets - Validation.csv, Train.csv, and Test.csv - this data set provides invaluable insights into the cricket commentary scenario through text classification and natural language processing methods. The comprehensive features available in this dataset enable researchers to examine everything from player performance and team strategies to fan excitement patterns over various matches or tournaments. Furthermore, associating context-specific sentiment analysis results with each individual comment provides a greater depth of insight than ever before possible! Use this source of analysis to uncover previously unseen trends in cricket commentary today!
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How to use the dataset
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Download the three datasets – Test.csv, Train.csv, Validation.csv – from Kaggle into a single folder on your local computer or Google Drive.
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For text pre-processing, clean up the data by removing any punctuation marks or stop words in order to generate a clean dataset for further analysis.
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Use either supervised or unsupervised machine learning algorithms such as Naive Bayes or Support Vector Machines (SVMs) to create models based on your chosen technique that can provide predictive classifications of new cricket commentary records within our datasets.
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Enter in parameters corresponding with the desired classifications you are attempting to make predictions for; use train and validation data for this process where applicable.
a) If you are using supervised learning algorithms split the data into training set (Train.csv), validation sets (Validation .cs‌v), and test sets (Test .csv). Furthermore define your labels within each of these files before training starts so that all files share matching labels
b) In case of unsupervised learning clusters classification techniques visualize your labelled training set(Train_data_labelled file ) in 2D using PCA from sklearn library before model building continues
- Generate insights after executing feature engineering appropriate ML algorithm relevant approaches such as sentiment analysis etc; based off what questions you are trying answer through generated results then evaluate performance metrics
6 ) Lastly once results have been analysed and stored as per user needs access deployable server by deploying trained model along with necessary libraries so that it can be accessed remotely from anywhere as an api response depending upon need
Research Ideas
- Generating sentiment analysis of the commentators’ remarks about the cricket match - this can help cricket commentators make their derision more unbiased and track dismissed players’ emotional reactions.
- Creating analytics on which type of commentary garners the most engagement from viewers, so commentators can know how to drive viewership during a particular match.
- Invoking natural language processing techniques to detect actionable insights in commentary data, such as score patterns or winning team correlations that could be used to advise and inform commentating decisions going forward
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: validation.csv
File: train.csv
File: test.csv
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Huggingface Hub.