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Text Mining Techniques for Car Price Prediction

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Resumo:Modern data sources routinely contain information both in unstructured and structured forms, combining text with the usual numerical and categorical data. For instance, in websites dedicated for selling and buying cars the listings typically include a textual description of the car. Others also include a detailed list of numerical or categorical attributes, such as the total number of kilometers the car has, or it´s model. In this work project we apply text mining techniques to create predictors for car price regression from unstructured data, the textual description in car listings. Two different types of predictors were studied, the tf-idf features obtained from the n-gram count matrix, or the singular vectors derived from the decomposition of the tf-idf matrix. In this work we also examine the performance of reducing the vocabulary dimension by applying stemming, lemmatization or not applying either of those. We also compare the effects of creating the initial n-gram count matrix with only unigrams, unigrams and bigrams or only bigrams. Our regression experiment shows that Support Vector Regression performs best at car price prediction using text data as predictors with R2 = 0.77, MSE = 0.19 and MAE = 0.32. These results can be seen as respectable given the complex nature of the task.
Autores principais:Gonçalves, Ricardo Miguel Galvão
Assunto:Text Mining Regression Analysis Car Price Prediction
Ano:2022
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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