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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

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Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm.

Ahmed Al-Saffar¹, Suryanti Awang¹, Hai Tao¹

¹Faculty of Computer System and Software Engineering, University Malaysia Pahang UMP, Pahang, Malaysia.

|April 24, 2018

Summary

This summary is machine-generated.

This study introduces a Malay sentiment analysis model using machine learning and lexicon-based approaches. Feature extraction significantly enhances classification accuracy for Malay reviews.

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Area of Science:

Natural Language Processing
Computational Linguistics
Machine Learning

Background:

Sentiment analysis is crucial for review aggregation websites.
Existing methods require adaptation for low-resource languages like Malay.
Automated analysis of Malay opinion text is challenging.

Purpose of the Study:

To propose an improved Malay sentiment analysis classification model.
To enhance classification performance using semantic orientation and machine learning.
To develop a robust system for analyzing Malay sentiment.

Main Methods:

A Malay sentiment lexicon of 2,478 phrases/words was created and manually scored.
Combined lexicon knowledge and supervised machine learning approaches were used.
Thirteen features were evaluated using individual and combined classifiers on the Malay Reviews Corpus (MRC).

Main Results:

Feature extraction improved Malay sentiment analysis performance.
The combined classification approach demonstrated superior results.
Classification accuracy was influenced by features, feature count, and classification method.

Conclusions:

The proposed model effectively improves Malay sentiment analysis.
Combining lexicon-based and machine learning methods is beneficial.
Further research can optimize feature selection and classification strategies.