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All Journal Indonesian Journal of Chemistry Journal of Applied Data Sciences
Le, Thuy Thi
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Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Object-Level Sentiment Analysis Use a Language Model Le, Thuy Thi; Phan, Tuoi Thi
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.448

Abstract

Sentiment analysis remains a prominent area of research in the natural language processing (NLP) community and holds significant practical value in domains such as commerce and education. Most existing approaches evaluate sentiments for a single object or product, typically categorizing them as positive or negative. However, when a text involves comparisons between multiple objects, it can be challenging to identify which sentiment or emotion is associated with which object. Few studies have addressed this issue, often stopping at evaluating emotions at the sentence level or for individual words related to aspects or objects. This study proposes an object-level sentiment analysis problem that produces a set of pairs or triples consisting of an object, aspect, and sentiment. Additionally, in texts expressing opinions or comments on a specific aspect, the aspect may be implied through references to the object without being explicitly mentioned. Identifying such implicit aspects is crucial, as it ensures no loss of information and enhances the efficiency of extraction of information in object-level sentiment analysis. The integration of implicit aspect identification and object-level sentiment analysis is the primary focus of this research. In recent years, many language models have been developed and effectively applied to various NLP tasks. Therefore, to address the proposed challenges, this study utilizes deep learning that incorporates language models combined with NLP methods such as parsing and dependency analysis, to achieve the desired output. Using language model and NLP techniques automatically generate training data for the learning model. The proposed method achieves an accuracy of 90%, making a substantial contribution to the field of NLP.
Iron-Binding Capacity and Antidiabetic Activity of Baby Clam (Corbiculidae sp.) Meat Protein Hydrolysate Vo, Tam Dinh Le; Huynh, Thu; Le, Thuy Thi; Tran, An Thi Tuong; Vo, Bao Chi
Indonesian Journal of Chemistry Vol 25, No 1 (2025)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijc.98947

Abstract

Baby clam (Corbiculidae sp.) meat is served as a traditional dish in Vietnam, and the antioxidant activity of its protein extract has been discovered. This study evaluates baby clam meat protein hydrolysate's iron-binding capacity (IBC) and antidiabetic activity. Initially, an analysis of the basic chemical composition of the meat was conducted. Subsequently, Alcalase was employed for hydrolysis. The highest IBC and α-amylase inhibition activity were targets for obtaining the best hydrolysis condition, including the clam meat-to-water ratio, enzyme-to-substrate (E:S) ratio, and time. Under the best condition, the hydrolysates demonstrated the IBC of 1246.20 ± 44.00 µg Fe2+/g protein and α-amylase inhibition activity of 48.33 ± 1.44%, approaching three-quarters of the activity of ethylenediaminetetraacetic acid (EDTA) sodium salt and acarbose, respectively. These results served as preliminary data for the development of the protein hydrolysates as a natural iron chelator or α-amylase inhibitor, which could support the treatment of iron deficiency and diabetes.
Co-Authors Huynh, Thu Phan, Tuoi Thi Tran, An Thi Tuong Vo, Bao Chi Vo, Tam Dinh Le