MELex: a new lexicon for sentiment analysis in mining public opinion of Malaysia affordable housing projects
Sentiment analysis has the potential as an analytical tool to understand the preferences of the public. It has become one of the most active and progressively popular areas in information retrieval and text mining. However, in the Malaysia context, the sentiment analysis is still limited due to the...
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| Format: | Thesis |
| Language: | eng eng eng |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://etd.uum.edu.my/8682/1/Deposit%20Permission_s900986.pdf https://etd.uum.edu.my/8682/2/s900986_01.pdf https://etd.uum.edu.my/8682/3/s900986_references.docx |
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| Summary: | Sentiment analysis has the potential as an analytical tool to understand the preferences of the public. It has become one of the most active and progressively popular areas in information retrieval and text mining. However, in the Malaysia context, the sentiment analysis is still limited due to the lack of sentiment lexicon. Thus, the focus of this study is to a new lexicon and enhance the classification accuracy of sentiment analysis
in mining public opinion for Malaysia affordable housing project. The new lexicon for sentiment analysis is constructed by using a bilingual and domain-specific sentiment lexicon approach. A detailed review of existing approaches has been conducted and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. The developed approach is able to analyze text for two most widely used languages in Malaysia, Malay and English, with better accuracy. The process of constructing MELex involves three activities: seed words selection, polarity assignment and synonym expansions, with four different experiments have been implemented. It is evaluated based on the experimentation and case study approaches where PR1MA and PPAM are selected as case projects. Based on the comparative results over 2,230 testing data, the study reveals that the classification using MELex
outperforms the existing approaches with the accuracy achieved for PR1MA and PPAM projects are 90.02% and 89.17%, respectively. This indicates the capabilities of MELex in classifying public sentiment towards PRIMA and PPAM housing projects. The study has shown promising and better results in property domain as compared to the previous research. Hence, the lexicon-based approach implemented
in this study can reflect the reliability of the sentiment lexicon in classifying public sentiments. |
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