An evaluation of machine learning and latent semantic analysis in text sentiment classification

• Autorzy: Miazga Justyna , Hachaj Tomasz

Identyfikatory

DOI

10.37705/TechTrans/e2020030

Warianty tytułu

PL

[Ewaluacja uczenia maszynowego i utajonej analizy semantycznej w klasyfikacji sentymentu tekstu]

• Języki publikacji: EN

Abstrakty

EN

In this paper, we compare the following machine learning methods as classifiers for sentiment analysis: k – nearest neighbours (kNN), artificial neural network (ANN), support vector machine (SVM), random forest. We used a dataset containing 5,000 movie reviews in which 2,500 were marked as positive and 2,500 as negative. We chose 5,189 words which have an influence on sentence sentiment. The dataset was prepared using a term document matrix (TDM) and classical multidimensional scaling (MDS). This is the first time that TDM and MDS have been used to choose the characteristics of text in sentiment analysis. In this case, we decided to examine different indicators of the specific classifier, such as kernel type for SVM and neighbour count in kNN. All calculations were performed in the R language, in the program R Studio v 3.5.2. Our work can be reproduced because all of our data sets and source code are public.

Słowa kluczowe

EN

sentiment analysis; machine learning; sentiment classification; K-nearest neighbours; neural network; support vector machine

PL

analiza nastrojów; nauczanie maszynowe; sieć neuronowa; maszyna wektorów nośnych

• Wydawca: Wydawnictwo Politechniki Krakowskiej im. Tadeusza Kościuszki
• Czasopismo: Technical Transactions
• Rocznik: 2020
• Tom: Vol. 117, iss. 1
• Strony: art. no. e2020030
• Opis fizyczny: Bibliogr. 23 poz., wykr., tab., wz.

Twórcy

autor

Miazga Justyna

• Institute of Computer Science, Pedagogical University of Cracow

autor

Hachaj Tomasz

• Institute of Computer Science, Pedagogical University of Cracow

Bibliografia

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• Miazga, J., & Hachaj, T. (n.d.). Datasets and source code used in this article. Retrieved from https://github.com/JusMia/sentimentanalysis_ML (August 20, 2020).
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• Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117–126.
• Trsteniak, B., Mikac, S., & Donko, D. (2014). KNN with TF-IDF based Framework for Text Categorization. Procedia Engineering, 69, 1356–1364.
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Uwagi

Section "Computer Sciences"

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).

Wariant tytułu uzyskany od autora, nie uwzględniony w artykule.