A Novel Approach for Classification of Online Product Reviews using various Machine Learning Techniques

Prathyakshini,; K, Preethi Salian; R, Puneeth B.; Nargis, Tanzila; Salian, Supriya

doi:10.1109/iceca55336.2022.10009447

Cited by 5 publications

(6 citation statements)

References 20 publications

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“…Dataset size [23], [25], [29], [31], [44], [45] <5,000 [20], [22], [26], [30], [43], [46] 5,000-20,000 [24], [36], [38], [47] 20,000-50,000 [21] 50,000-100,000 [33], [34], [35], [40] >100,000 [27], [28], [32], [37], [39], [41], [42] Not available…”

Section: Literaturementioning

confidence: 99%

“…Performance metric [21], [25], [41], [47] Accuracy [28], [32] Accuracy, precision, recall [30], [44] Accuracy, F1-score [20], [23], [24], [26], [29], [31], [36], [37] Accuracy, precision, recall, F1-score [22], [27] Precision, recall, F1-score [38] F1-score [34] Perception score [46] Mapping quality characteristic [40] Relationship (online reviews-hotel sales) [43] Classify user component [33] Confusion matrix Consequently, it is deemed proficient at classifying forthcoming values. According to the data presented in Table 8, it can be observed that the random forest technique exhibits the highest level of accuracy among the ML algorithms, achieving a remarkable accuracy rate of 99.2% when applied to the TripAdvisor dataset.…”

Section: Literaturementioning

confidence: 99%

“…The data presented in Table 8 also demonstrates that most of the ML algorithms achieved accuracy levels exceeding 90%. Tripadvisor Random forest 99.20% [30] ATIS and Snips BERT 98.8% [36] Mobile banking SVM 97.17% [26] Twitter LSTM 97% [23] E-commerce LSTM 96.92% [44] E-commerce SVM 96.57% [33] Web news SVM 95.04% [31] ATIS SVM 95% [24] Amazon Random forest 94% [47] Amazon CNN 92.30%…”

Section: Literaturementioning

confidence: 99%

See 2 more Smart Citations

Text Classification on Customer Feedback: A Systematic Literatures Review

Sidek,

Syed Ahmad,

Kumar

et al. 2024

IJEECS

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User feedback in text classification serves multiple purposes, including refining models, enhancing datasets, adapting to user preferences, identifying emerging topics, and evaluating system performance, all of which contribute to the creation of more effective and user-centric classification systems. Many text classification techniques, including data mining, machine learning, and deep learning approaches, have been employed in previous literature, each making significant contributions to the field. This paper aims to contribute by guiding researchers seeking commonly used classification techniques and evaluation metrics in text processing. Additionally, it identifies the classification technique that generates higher accuracy and works as a basis for researchers to synthesize studies within their respective fields. PRISMA methodology is adapted to systematically review 28 current literatures on text classification on user feedback. The results obtained are guided by four research questions; paper distribution year, dataset source and size; evaluation metric and model accuracy. The review has shown that support vector machines (SVM) are frequently employed and consistently achieve high levels of accuracy as high as 97.17% with various datasets used. The future direction of this work could explore models that integrate sentiment analysis and natural language understanding to more accurately capture nuanced user opinions and preferences.

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Section: Literaturementioning

confidence: 99%

Section: Literaturementioning

confidence: 99%

Section: Literaturementioning

confidence: 99%

See 1 more Smart Citation

Text Classification on Customer Feedback: A Systematic Literatures Review

Sidek,

Syed Ahmad,

Kumar

et al. 2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…The Simplest Implementation Method: Most of the projects are not able to implement directly to their parking areas, because it's related to specified areas. But in this project, it allows everyone to implement it directly, without making any changes, because it's simple, common, efficient and affordable to everyone [14]- [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Parking System: A Simple Way to Manage Parking Area Using Internet of Things

2023

IRJMETS

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The need for parking spots has increased as a result of cities' fast urbanisation and rising population, particularly in congested locations like theatres, malls, and other well-liked tourist attractions. The use of a smart parking system has been highly praised as a solution to these problems. This system makes use of Infrared (IR) sensors as well as other cutting-edge technologies to locate and reserve parking spaces, giving drivers up-to-the-minute information on available places. Additionally, the Smart Parking System makes it possible to monitor and manage parking spaces effectively, improving safety and traffic flow. This technology improves customer happiness by decreasing the time and fuel used to hunt for parking and helps to create a more sustainable urban environment.

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“…According to the Food and Agriculture Organization of the United Nations, approximately 91-93 million tonnes of fish are harvested worldwide each year, with roughly one-third of fisheries currently overfished and approximately 60 percent fished to capacity [3] [4]. IUU fishing is the world's sixth-largest crime, accounting for up to one-fifth of all fish caught at sea and estimated to be worth up to $23.5 billion per year [5] [6]. Commercial fishing industry management has historically been complicated and costly, but technology and openness are bringing about real transformation.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Identifying and Preventing Illegal Fishing

2023

IRJMETS

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Commercial fishing is the most common type of maritime human activity. Commercial fishing is the for-profit activity of harvesting fish or other marine species using boats, and its effects extend far beyond maintaining ocean health and managing fish populations. The monitoring of marine resources is a critical concern for marine resource management. Illegal fishing operations are prohibited. Overfishing and illegal, unreported, and unregulated (IUU) fishing pose serious threats to the sustainability of fish populations and other marine resources. Commercial fishing industry management has historically been complicated and costly, but technology and openness are bringing about real transformation. Using a variety of machine learning methods, one or more pursuers are trained to successfully catch an evader. This method is used to replicate actual illegal and unreported fishing chase events on both the pursuer and evader vessel agents. The goal of this effort is to reduce and eliminate cross fishing and illegal fishing. The Automatic Identification System, which is gathered by satellites and terrestrial receivers, may detect specific fishing activity based on the movement of vessels over time. Each vessel sends that information out on a regular basis.

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A Novel Approach for Classification of Online Product Reviews using various Machine Learning Techniques

Cited by 5 publications

References 20 publications

Text Classification on Customer Feedback: A Systematic Literatures Review

Text Classification on Customer Feedback: A Systematic Literatures Review

Smart Parking System: A Simple Way to Manage Parking Area Using Internet of Things

A Machine Learning Approach to Identifying and Preventing Illegal Fishing

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