Analyzing user reviews in Thai language toward aspects in mobile applications

Deewattananon, Boonyarit; Sammapun, Usa

doi:10.1109/jcsse.2017.8025903

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…Another method proposed for sentiment classification is the utilization of cosine similarity, to classify the sentiment expressed by a user's comment [17]. Furthermore, there exist a significant number of related studies [18][19][20][21] that primarily focus on mining customer reviews and determining the polarity of the opinions expressed i n them.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment analysis of the awareness of environmental sustainability

Kularbphettong,

Roonrakwit,

Boonseng

2024

2576-8484

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This study examines the sentiment analysis of awareness of environmental sustainability. Environmental sustainability is the responsible management and utilization of Earth's natural resources to meet the needs of the present generation and ensure that future generations will access those resources. The awareness of environmental sustainability has been growing globally as people, businesses, and governments recognize the importance of preserving the planet for current and future generations. Sentiment analysis of environmental sustainability involves evaluating opinions, attitudes, and emotions expressed in texts related to environmental sustainability, and analyzing sentiment can provide insights into public perception, awareness, and engagement with environmental issues. This exploratory study's primary goal is to conduct social media opinion mining in the context of Thai people's environmental sustainability. The paper presented how to build a model of sentiment analysis with linguistic analysis, including data preprocessing steps, feature extraction, and model constructions. The techniques used in this research include Logistic Regression, Random Forests, Support Vector Machine, Word Segmentation and Bag of Words. The result shows that the model is able to categorize sentiment analysis opinions in the sustainability context primarily in positive terms. The positive sentiments suggest a sustained, long-term shift in awareness, or they might be influenced by specific events or trends. However, positive sentiment analysis results are expressed towards environmental sustainability initiatives, such as renewable energy projects, waste reduction efforts, or conservation programs. Moreover, public awareness plays a crucial role in influencing individual behavior, corporate practices, and government policies towards a more sustainable and environmentally conscious future.

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Section: Literature Reviewmentioning

confidence: 99%

Sentiment analysis of the awareness of environmental sustainability

Kularbphettong,

Roonrakwit,

Boonseng

2024

2576-8484

View full text Add to dashboard Cite

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“…Only three out of 70 studies explicitly answer a question from the initial Microsoft study. The second sub-table in Table 2 shows that only 3 studies (4%) explicitly refer their research question to an initial Microsoft one: [16,28,33]. Nine studies (13%) partly try to answer a MS question: [8-10, 30, 52, 62, 64, 65, 70].…”

Section: Impact Of the Microsoft 2014 Studymentioning

confidence: 99%

Questions for data scientists in software engineering: a replication

Huijgens

Rastogi

Mulders

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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In 2014, a Microsoft study investigated the sort of questions that data science applied to software engineering should answer. This resulted in 145 questions that developers considered relevant for data scientists to answer, thus providing a research agenda to the community. Fast forward to five years, no further studies investigated whether the questions from the software engineers at Microsoft hold for other software companies, including software-intensive companies with different primary focus (to which we refer as software-defined enterprises). Furthermore, it is not evident that the problems identified five years ago are still applicable, given the technological advances in software engineering.This paper presents a study at ING, a software-defined enterprise in banking in which over 15,000 IT staff provides in-house software solutions. This paper presents a comprehensive guide of questions for data scientists selected from the previous study at Microsoft along with our current work at ING. We replicated the original Microsoft study at ING, looking for questions that impact both software companies and software-defined enterprises and continue to impact software engineering. We also add new questions that emerged from differences in the context of the two companies and the five years gap in between. Our results show that software engineering questions for data scientists in the software-defined enterprise are largely similar to the software company, albeit with exceptions. We hope that the software engineering research community builds on the new list of questions to create a useful body of knowledge. CCS CONCEPTS• General and reference → Surveys and overviews.

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“…(1) Low Accuracy of Language Processing Tools. The aforementioned unique characteristics of Thai such as, Thai text does not contain any word/sentence boundary, makes it harder for Thai language processing tools such as TLTK 2 to yield a high accuracy [5,6,16,18]. Consequently, the stylometric features extraction process for Thai is noisier in comparison to English.…”

Section: Limitations Of Existing Studymentioning

confidence: 99%

“…The preprocessing part of our solution transforms each document into a collection of fragments (i.e., collection of point sets) using a three steps process [33]: (i) partition each document into fxed size fragments; (ii) partition each fragment obtained from the frst step into fxed size chunks 4 ; and (iii) extract the 46 stylometric features (i.e., writing style markers) from each chunk. To obtain reliable stylometric statistics from each fragment and the chunk, we fx their sizes to 7,000 and 700 tokens 5 , respectively. As a result, each chunk is transformed into a point, each fragment is transformed into a point set and each document is transformed into a collection of point sets in 46-dimensional space.…”

Section: Preprocessingmentioning

confidence: 99%

“…Note that, in our stylometric feature space given in Table 3, 32 out of 46 features (i.e., except the character-based features from # 14 to # 27) require the word tokenization and sentence identifcation, which is a challenging task to perform due to the reason that there are no word/sentence boundaries in Thai. These characteristics of Thai make it harder for Thai language processing tools such as TLTK 6 to yield a high accuracy [5,6,16,18]. Consequently, the stylometric features extraction process for Thai is noisier in comparison to English.…”

Section: Preprocessingmentioning

confidence: 99%

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StyloThai:

Sarwar

Porthaveepong

Rutherford

et al. 2020

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Authorship identification helps to identify the true author of a given anonymous document from a set of candidate authors. The applications of this task can be found in several domains, such as law enforcement agencies and information retrieval. These application domains are not limited to a specific language, community, or ethnicity. However, most of the existing solutions are designed for English, and a little attention has been paid to Thai. These existing solutions are not directly applicable to Thai due to the linguistic differences between these two languages. Moreover, the existing solution designed for Thai is unable to (i) handle outliers in the dataset, (ii) scale when the size of the candidate authors set increases, and (iii) perform well when the number of writing samples for each candidate author is low. We identify a stylometric feature space for the Thai authorship identification task. Based on our feature space, we present an authorship identification solution that uses the probabilistic k nearest neighbors classifier by transforming each document into a collection of point sets. Specifically, this document transformation allows us to (i) use set distance measures associated with an outlier handling mechanism, (ii) capture stylistic variations within a document, and (iii) produce multiple predictions for a query document. We create a new Thai authorship identification corpus containing 547 documents from 200 authors, which is significantly larger than the corpus used by the existing study (an increase of 32 folds in terms of the number of candidate authors). The experimental results show that our solution can overcome the limitations of the existing solution and outperforms all competitors with an accuracy level of 91.02%. Moreover, we investigate the effectiveness of each stylometric features category with the help of an ablation study. We found that combining all categories of the stylometric features outperforms the other combinations. Finally, we cross compare the feature spaces and classification methods of all solutions. We found that (i) our solution can scale as the number of candidate authors increases, (ii) our method outperforms all the competitors, and (iii) our feature space provides better performance than the feature space used by the existing study.

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Analyzing user reviews in Thai language toward aspects in mobile applications

Cited by 9 publications

References 11 publications

Sentiment analysis of the awareness of environmental sustainability

Sentiment analysis of the awareness of environmental sustainability

Questions for data scientists in software engineering: a replication

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