<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Technical Transactions

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

In this paper, we compare the following machine learning methods as classifiers for sentiment analysis: k – nearest neighbours (kNN), artificial neural network...

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

Online veröffentlicht: 01. Okt. 2020

Seitenbereich: -

Eingereicht: 20. Juni 2020

Akzeptiert: 22. Sept. 2020

DOI: https://doi.org/10.37705/TechTrans/e2020030

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

© 2020 Justyna Miazga et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

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

Online veröffentlicht: 01. Okt. 2020

Seitenbereich: -

Eingereicht: 20. Juni 2020

Akzeptiert: 22. Sept. 2020

DOI: https://doi.org/10.37705/TechTrans/e2020030

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

© 2020 Justyna Miazga et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

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