Exerting 2D-Space of Sentiment Lexicons with Machine Learning Techniques: A Hybrid Approach for Sentiment Analysis

Khan, Muhammad Yaseen; Junejo, Khurum Nazir

doi:10.14569/ijacsa.2020.0110672

Cited by 8 publications

(6 citation statements)

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“…[17] researched on food preference, [18] examined star rating on restaurants, [13] studied incentive hierarchies affect user behaviour, [19] compared China and USA tourist satisfaction in a restaurant, and many studies are being conducted to improve algorithm accuracy [20]- [25]. Other than English, most research has been conducted in Arabic [26]- [29]; French language [30]- [32]; Chinese language [33]- [35] and Italian [36]- [38]. On the contrary, in Malay language there are not much research especially in processing a text mixture English and Malay language simultaneously.…”

Section: Problem Issues and Statementmentioning

confidence: 99%

Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining

Abdul Rahman,

A. Talip,

Sarirah

2024

IJPCC

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Online consumer reviews in e-commerce are one technique to gather consumer opinion and sentiment about a company's products and services. However, manual analysis is impractical due to natural language text's enormous volume and complexity. Text mining and sentiment analysis methods based on machine learning provide an opportunity to analyze data for marketing objectives by increasing sales, positive electronic word-of-mouth (e-WOM), and meeting consumer demands and wants through the enhancement of market offerings. Despite the numerous benefits of analyzing e-commerce reviews to assist a company's marketing strategy, very little research has focused on sentiment and acceptance for Malaysia’s local agriculture products due to mixed language (English-Malay language) processing challenges. This concept paper highlights the use of text mining techniques to extract valuable insights from e-commerce comments related to Malaysian local agriculture products. By leveraging text mining, the study aims to better understand consumer sentiments, preferences, and feedback regarding local products, thereby facilitating improved market analysis and decision-making processes.

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Section: Problem Issues and Statementmentioning

confidence: 99%

Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining

Abdul Rahman,

A. Talip,

Sarirah

2024

IJPCC

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“…Khan and Junejo [38] conducted an experiment utilizing a hybrid classification approach, which incorporated lexiconbased scores of positive and negative classes for supervised learning. The results showed a notable improvement of 7.88% and 1.7% for the hybridized approach compared to the lexiconbased approach and machine learning-based approach, respectively.…”

Section: A Sentiment Classificationmentioning

confidence: 99%

“…There has been much research on sentiment classification in which the F1-score has been used as the main evaluation metric, as demonstrated in works like [18], [38], [78]. However, since the dataset used in this paper is slightly imbalanced, the authors are placing more emphasis on achieving a higher balanced accuracy [83], which is calculated as the arithmetic mean of the true positive rate and true negative rate [91]- [93].…”

Section: ) Evaluation Criteriamentioning

confidence: 99%

Cognitive Relationship-Based Approach for Urdu Sarcasm and Sentiment Classification

Khan,

Ahmed,

Siddiqui

et al. 2023

IEEE Access

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Humans have a natural tendency to express their emotions, but they are also skilled at using sarcasm to shape their feelings. In cognitive computing and natural language processing research, sentiment analysis and sarcasm detection are typically treated as separate tasks, with each text analyzed in isolation. However, this approach overlooks the connection between sentiment and sarcasm. We believe that sentiment and sarcasm are closely related and should be analyzed together to achieve a better understanding of context and natural language. In this paper, we propose a new framework that leverages the Cognitive Relationship (CR) between sarcasm and sentiment to improve the accuracy of classification. By taking into account the relationship between these two factors, we can achieve better results in sentiment analysis and sarcasm detection. We have also created a new and nearly balanced dataset for sentiment and sarcasm classification in standard Urdu that contains 7,000 tweets, which make up over 210K tokens. To gain a better understanding of the data, we conducted exploratory data analysis on words, hashtags, and emojis. The proposed methodology conducted a variety of classical machine learning classifiers and tested them with different variations of the dataset. After a thorough analysis of the results and errors, we found that the CR-based approach for sarcasm and sentiment classification performed better than the traditional stand-alone (SA) approach. Among the classifiers, Linear Regression and eXtreme Gradient Boosting proved to be the most effective. The sentiment classification based on CR has demonstrated a 9.3% enhancement compared to the stand-alone (SA) method while maintaining an overall improvement of approximately 22% compared to the baseline distribution. In the same way, the sarcasm classification based on CR has shown a 9.1% improvement over the SA approach and approximately 23.6% improvement over the baseline distribution.

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“…It is well suited for the classification of complex, imbalanced ones but should be small or medium-sized datasets. e SVM aims to draw a hyperplane in an n-dimensional vector space, such that the hyperplane separates data points into two distinct partitions of data, representing the respective classes [25]. e SVM can be used for linear or nonlinear classification.…”

Section: Machine Learning Algorithms K-nearest Neighboursmentioning

confidence: 99%

“…e SVM can be used for linear or nonlinear classification. However, the basic SVM, which fits a hyperplane, is conventionally known as linear-SVM [25,26]. Equation (12) gives the mathematical semantics for understanding linear-SVM.…”

Section: Machine Learning Algorithms K-nearest Neighboursmentioning

confidence: 99%

Automated Prediction of Good Dictionary EXamples (GDEX): A Comprehensive Experiment with Distant Supervision, Machine Learning, and Word Embedding‐Based Deep Learning Techniques

et al. 2021

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Dictionaries not only are the source of getting meanings of the word but also serve the purpose of comprehending the context in which the words are used. For such purpose, we see a small sentence as an example for the very word in comprehensive book-dictionaries and more recently in online dictionaries. The lexicographers perform a very meticulous activity for the elicitation of Good Dictionary EXamples (GDEX)—a sentence that is best fit in a dictionary for the word’s definition. The rules for the elicitation of GDEX are very strenuous and require a lot of time for committing the manual process. In this regard, this paper focuses on two major tasks, i.e., the development of labelled corpora for top 3K English words through the usage of distant supervision approach and devising a state-of-the-art artificial intelligence-based automated procedure for discriminating Good Dictionary EXamples from the bad ones. The proposed methodology involves a suite of five machine learning (ML) and five word embedding-based deep learning (DL) architectures. A thorough analysis of the results shows that GDEX elicitation can be done by both ML and DL models; however, DL-based models show a trivial improvement of 3.5% over the conventional ML models. We find that the random forests with parts-of-speech information and word2vec-based bidirectional LSTM are the most optimal ML and DL combinations for automated GDEX elicitation; on the test set, these models, respectively, secured a balanced accuracy of 73% and 77%.

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Exerting 2D-Space of Sentiment Lexicons with Machine Learning Techniques: A Hybrid Approach for Sentiment Analysis

Cited by 8 publications

References 0 publications

Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining

Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining

Cognitive Relationship-Based Approach for Urdu Sarcasm and Sentiment Classification

Automated Prediction of Good Dictionary EXamples (GDEX): A Comprehensive Experiment with Distant Supervision, Machine Learning, and Word Embedding‐Based Deep Learning Techniques

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