Abstract
This study proposes an improvement to the Insight Centre for Data Analytics algorithm, which identifies the most relevant topics in a corpus of tweets, and allows the construction of search rules for that topic or topics, in order to build a corpus of tweets for analysis. The improvement shows above 14% improvement in Purity and other metrics, and an execution time of 10% compared to Latent Dirichlet Allocation (LDA).
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Notes
- 1.
Relatively homogeneous “natural” groupings in a statistical population.
- 2.
A collection of language pieces that are selected according to explicit linguistic criteria with the purpose of being used as a language sample.
- 3.
As the dimensions increase, data becomes sparse. Therefore, the amount of data that is needed must increase exponentially with it or there will not be enough points in the sample of more space for any type of analysis to perform.
- 4.
The ngram ‘ganas tomar Corona’, refer literally to ‘want drink Corona’, where Corona is a beer produced in Mexico.
- 5.
The ngram ‘gustado video youtube’, refer literally to ‘liked video youtube’.
- 6.
An ngram is considered important enough if it “passes” a function of our frequency parameters defined at the start. At the very least, the ngram must be repeated 5 times.
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Acknowledgments
This work has been supported by Asociación Mexicana de Cultura A.C. and Consejo Nacional de Ciencia y Tecnología (CONACyT).
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Martínez, E.D., Fonseca, J.P., González, V.M., Garduño, G., Huipet, H.H. (2017). Detection of Topics and Construction of Search Rules on Twitter. In: Solano, A., Ordoñez, H. (eds) Advances in Computing. CCC 2017. Communications in Computer and Information Science, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-319-66562-7_13
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