Feature-Based Opinion Mining on Smart Phone Reviews

Saensuk, Mongkol; Songram, Panida; Chomphuwiset, Phatthanaphong

doi:10.12792/icisip2015.047

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…For example, "very gooddddddd" converts to "very good" (and can be added to the dictionary). Research includes Kongyoung et al (2019) and Saensuk et al (2019).…”

Section: Lextoplus Dictionary-based Approachmentioning

confidence: 99%

“…However, the use of word attribute does not consider word placement, word efficiency, or quality in constructing a discriminant model. The verbal feature has also been used in research samples (Chumwatana, 2015;Masdisornchote, 2016;Laosungsuk et al, 2017;Saensuk et al, 2019).…”

Section: Wordmentioning

confidence: 99%

“…The individual received points if the answer that matched the keyword was found (see Table 2). Saensuk et al (2019) developed a method for characterization and classifying opinions on smartphone features into positive and negative statements. Different types of words ("pretty" or "bad") can be positive and negative, respectively.…”

Section: Lexicon-based Approach (Lb Approach)mentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Automatic Text Classification Based on Thai Social Media Data

Tanantong

Parnkow

2022

International Journal of Knowledge and Systems Science

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In the digital age, the information on social media, such as Facebook, Twitter, and Instagram, is increasing rapidly. Therefore, it has led to studies and researches on social media analytics to extract useful models or knowledge from the data. One of the most interesting topics in social media analytics is text classification on social media data. However, since social media data has a diverse and complex data structure, text analysis and classification are considered a challenging issue that requires a specific technique to implement. The objective of this review paper is to collect and review research related to the automatic classification of Thai text on social media by presenting and explaining the process of text classification on various issues. These include data collection and data sources, amount of data and data preparation for research, feature extraction methods, text classification automated modeling methods, efficacy evaluation and measurement methods, the results of text classification, and summary of the overall trend of research on the topic.

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“…For example, "very gooddddddd" converts to "very good" (and can be added to the dictionary). Research includes Kongyoung et al (2019) and Saensuk et al (2019).…”

Section: Lextoplus Dictionary-based Approachmentioning

confidence: 99%

Section: Wordmentioning

confidence: 99%

Section: Lexicon-based Approach (Lb Approach)mentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Automatic Text Classification Based on Thai Social Media Data

Tanantong

Parnkow

2022

International Journal of Knowledge and Systems Science

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“…al. [11] exhibited traditional methodology and evaluated opinions logically for each sentence; they considered opinion is positive by comparing sentences and the result will be positive only if both of them are positive, else negative.…”

Section: Related Workmentioning

confidence: 99%

Feature-Based Sentiment Analysis for Arabic Language

Alhamad¹,

Kurdy²

2020

IJACSA

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In light of the spread of e-commerce and emarketing, and the presence of a huge number of reviews and texts written by people to share views on products, it became necessary to give attention to extracting these opinions automatically and analyzing the feelings of the reviewers. The goal is to obtain reports evaluating products and contribute to improve services at a glance. Sentiment Analysis is a relatively recent study that deals with the processing of natural texts published in web sites and social networks. However, the processing of texts written in the Arabic language is one of the challenges that specialists face because people do not rely on standard Arabic, writing people in spoken/colloquial languages and use various dialects. This paper will present feature-based sentiment analysis for Arabic language which works on text analysis technique that breaks down text into aspects (attributes or components of a product or service), and then allocates each one a sentiment level (positive, negative or neutral).

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“…In Mongkol [1] et al discussed feature based opining mining on smart phone reviews given in Thai language. Polarity of words is used to classify the reviews.…”

Section: Existing Related Workmentioning

confidence: 99%

Classification of Reviews on Mobile Phones Using Text Mining Techniques

Davis¹,

Vaidhehi²

2018

IJET

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People register their opinion or feedback regarding the products in different forum. This research work is based on the classification of reviews regarding the different mobile phones. Dataset from Amazon pertaining to the opinions for mobile phones is used in this work. Opinion which is expressed as text is classified as positive opinion or a negative opinion using text mining techniques. Opinion mining helps to understand the customers in a better way. This work shows the visual representation of words by using word cloud and to classify the reviews on a two point scale. From the dataset, randomly 197 reviews are taken out of which 148 reviews are classified as positive, 49 reviews are classified as negative.

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Feature-Based Opinion Mining on Smart Phone Reviews

Cited by 4 publications

References 5 publications

A Survey of Automatic Text Classification Based on Thai Social Media Data

A Survey of Automatic Text Classification Based on Thai Social Media Data

Feature-Based Sentiment Analysis for Arabic Language

Classification of Reviews on Mobile Phones Using Text Mining Techniques

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