The Generation of Malay Lexicon

Anbananthen, Kalaiarasi Sonai Muthu; Selvaraju, Surendran; Krishnan, Jaya Kumar

doi:10.3844/ajassp.2017.503.510

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…In a given sentence, the OS of a feature is calculated. Positive opinion words (Anbananthen et al, 2017) are assigned the polarity score of +1 and negative opinion words polarity score of −1. All the polarity score of the feature will be summed up by using the following score function as shown by Equation 1.0:…”

Section: Opinion Mappingmentioning

confidence: 99%

Opinion Extraction on Online Malay Text

Selvaraju¹,

Anbananthen²

2019

American Journal of Applied Sciences

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Growing of social media usage present a new set of opportunities and challenges in the way of information is retrieved and searched. Opinions on social media has become an important factor in influencing people choices on purchasing a product and service. Hence, sentiment analysis has become the most crucial tool in tracking people feedbacks on products and services. For Malay language there is limited sources available for this language. Thus, in this paper we present the method of extracting opinion on online Malay text. The traditional method using POS extraction is not adequate. Thus, rule based method is integrated with POS extraction method to improve opinion words extraction. Most of the existing tools are able to retrieve opinion at sentence and document level. More detail analysis is acquired to have detail information and summarization of a product. This is where feature level sentiment analysis is needed. The process of identifying opinion of a particular feature in a sentence, can be quite tedious and troublesome. This is because opinion of the feature can be hidden and scattered in the sentence. Therefore, opinion mapping is employed for opinion extraction at feature level in this paper. A set of tweets from telecommunication domain is used to evaluate the proposed framework. From the experiment, the accuracy of the extraction performed is 88%. The detail description of the feature level opinion extraction steps is discussed in this paper.

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Section: Opinion Mappingmentioning

confidence: 99%

Opinion Extraction on Online Malay Text

Selvaraju¹,

Anbananthen²

2019

American Journal of Applied Sciences

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Punctuation Restoration with Transformer Model on Social Media Data

et al. 2023

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Several key challenges are faced during sentiment analysis. One major problem is determining the sentiment of complex sentences, paragraphs, and text documents. A paragraph with multiple parts might have multiple sentiment values. Predicting the overall sentiment value for this paragraph will not produce all the information necessary for businesses and brands. Therefore, a paragraph with multiple sentences should be separated into simple sentences. With a simple sentence, it will be effective to extract all the possible sentiments. Therefore, to split a paragraph, that paragraph must be properly punctuated. Most social media texts are improperly punctuated, so separating the sentences may be challenging. This study proposes a punctuation-restoration algorithm using the transformer model approach. We evaluated different Bidirectional Encoder Representations from Transformers (BERT) models for our transformer encoding, in addition to the neural network used for evaluation. Based on our evaluation, the RobertaLarge with the bidirectional long short-term memory (LSTM) provided the best accuracy of 97% and 90% for restoring the punctuation on Amazon and Telekom data, respectively. Other evaluation criteria like precision, recall, and F1-score are also used.

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The Generation of Malay Lexicon

Cited by 2 publications

References 17 publications

Opinion Extraction on Online Malay Text

Opinion Extraction on Online Malay Text

Punctuation Restoration with Transformer Model on Social Media Data

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