Optimisation of Sentiment Analysis for E-Commerce

Text mining is a special case of data mining which explore unstructured or semi-structured text documents, to establish valuable patterns and rules that indicate trends and significant features about specific topics. Text mining has been in pattern recognition, predictive studies, sentiment analysis and statistical theories in many areas of research, medicine, financial analysis, social life analysis, and business intelligence. Text mining uses concept of natural language processing and machine learning. Machine learning algorithms have been used and reported to give great results, but their performance of machine learning algorithms is affected by factors such as dataset domain, number of classes, length of the corpus, and feature selection techniques used. Redundant attribute affects the performance of the classification algorithm, but this can be reduced by using different feature selection techniques and dimensionality reduction techniques. Feature selection is a data preprocessing step that chooses a subset of input variable while eliminating features with little or no predictive information. Feature selection techniques are Information gain, Term Frequency, Term Frequency-Inverse document frequency, Mutual Information, and Chi-Square, which can use a filters, wrappers, or embedded approaches. To get the most value from machine learning, pairing the best algorithms with the right tools and processes is necessary. Little research has been done on the effect of feature selection techniques on classification accuracy for pairing of these algorithms with the best feature selection techniques for optimal results. In this research, a text classification experiment was conducted using incident management dataset, where incidents were classified into their resolver groups. Support vector machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Decision tree (DT) machine learning algorithms were examined. Filtering approach was used on the feature selection techniques, with different ranking indices applied for optimal feature set and classification accuracy results analyzed. The classification accuracy results obtained using TF were, 88% for SVM, 70% for NB, 79% for Decision tree, and KNN had 55%, while Boolean registered 90%, 83%, 82% and 75%, for SVM, NB, DT, and KNN respectively. TF-IDF, had 91%, 83%, 76%, and 56% for SVM, NB, DT, and KNN respectively. The results showed that algorithm performance is affected by feature selection technique applied. SVM performed best, followed by DT, KNN and finally NB. In conclusion, presence of noisy data leads to poor learning performance and increases the computational time. The classifiers performed differently depending on the feature selection technique applied. For optimal results, the classifier that performed best together with the feature selection technique with the best feature subset should be applied for all types of data for accurate classification performance. Keywords: Text Classification, Supervised Machine Learning, Feature Selection DOI : 10.7176/JIEA/9-3-06 Publication date :May 31 st 2019

Sex determination of silkworm pupae is important for silkworm industry. Multivariate analysis methods have been widely applied in hyperspectral imaging spectroscopy for classification. However, these methods require essential steps containing spectra preprocessing or feature extraction, which were not easy determined. Convolutional neural networks (CNNs), which have been employed in image recognition, could effectively learn interpretable presentations of the sample without the need of ad-hoc preprocessing steps. The species of silkworm pupae are usually up to hundreds. Conventional classifiers based on one species of silkworm pupae could not give high performance when explored to other species that not participating in the model building, resulting in bad generalization ability. In this study, a CNN model was trained to automatically identify the sex of silkworm pupae from different years and species based on the hyperspectral spectra. The results were compared with the frequently used conventional machine classifiers including support vector machine (SVM) and K nearest neighbors (KNN). The results showed that CNN outperformed SVM and KNN in terms of accuracy when applied to the raw spectra with 98.03%. However, the performance of CNN decreased to 95.09% when combined with the preprocessed data. Then principal component analysis (PCA) was adopted to reduce data dimensionality and extract features. CNN gave higher accuracy than SVM and KNN based on PCA. The discussion section revealed that CNN had high generalization ability that could classify silkworm pupae from different species with a rather well performance. It demonstrated that HSI technology in combination with CNN was useful in determining the sex of silkworm pupae.

Cervical cancer is one of the most widely recognized gynecologic malignancies on the world and it is demanding since this malignant growth happens with no signs. As per World Health Organization (WHO), cervical cancer is the fourth most recurrent disease which is higher death rate that influenced women everywhere in the world. It has demonstrated that early discovery of any cancer when followed up with suitable diagnosis and treatment can expand the patient survival rate. But the existing techniques have problem with imbalanced dataset and feature selection-based classification accuracy. To conquer the previously mentioned issues, the existing strategies are analyzed different procedures of data mining and feature selection techniques which can be applied to bring out hidden information from the cervical cancer dataset. In this review, classification process and feature selection-based classification are performed to improve the given cervical cancer dataset accuracy significantly. In the classification process, the imbalanced data and redundant features are not handled effectively. Hence the feature selection-based classification is required to improve the cervical cancer classification accuracy. This survey is also analyzed the merits and shortcomings of each method applied to application. The comparative analysis is done using various classification techniques like Support Vector Machine (SVM), K Nearest Neighbor (KNN), Convolution Neural Network (CNN) and Synthetic Minority Oversampling Technique + Random Forest with Recursive Feature Elimination (SMOTE+RFE+RF) approach. The experimental result shows that the SMOTE+RFE+RF approach provides better performance in terms of higher accuracy, specificity, Positive Predicted Accuracy (PPA) and Negative Predicted Accuracy (NPA) and sensitivity rather than the other existing methods.

Variety purity is an important indicator in seed quality detection. Different varieties of corn seeds may be mixed in the growth and development process, which affects the growth and yield of the seeds. Thus, it is necessary to find a fast and non-destructively method to detect the purity. In this paper, the feasibility of combining hyperspectral imaging with deep convolutional neural network (DCNN) was studied to classify four corn seed varieties. Firstly, the average spectra from the region of seed in endosperm side hyperspectral images over the wavelength range of 450–979 nm were extracted. Secondly, the performances of three models were compared, including DCNN, K nearest neighbors (KNN) and support vector machine (SVM). DCNN model has the 100% training accuracy rate, 94.4% testing accuracy rate and 93.3% validation accuracy rate, and outperforms KNN and SVM models in most cases. DCNN model also had the best performance in evaluation indexes (sensitivity, specificity and precision). Finally, the visual classification map was generated according to the results of DCNN. Results show that DCNN can be adopted in spectral data analysis for the variety classification of corn seed; and the classification performance can be improved effectively.

Seed freezing damage is an agricultural disaster, which has a great impact on seed quality assurance. The feasibility of combining hyperspectral imaging with deep convolutional neural network (DCNN) to classify different freeze-damaged corn seeds was studied in this paper. At first, the hyperspectral images of corn seeds subjected to five different freezing temperatures at 400–1000 nm were acquired, and then the average spectra were extracted from the region of embryo hyperspectral images over the wavelength range of 450–979 nm. Next, four models (K nearest neighbors (KNN), support vector machine (SVM), extreme learning machine (ELM), and DCNN) were developed for five-category (5 frozen conditions) and four-category (“no freezing,” “slight freezing,” “moderate freezing,” and “severe freezing”) classifications, and the values of the evaluation indexes (accuracy, sensitivity, specificity, and precision) were calculated for comparison. The results show that DCNN model had the most satisfactory result with accuracy rates of 100% (training set), 96.9% (validation set), and 97.5% (testing set) for five-category classification, with accuracy rates of 100% (training, validation, and testing set) for four-category classification, and DCNN model also had the best performance in the evaluation indexes. At last, the visual classification map was generated according to the results of DCNN. It shows that hyperspectral imaging and DCNN can provide a novel method to detect the freezing damage of corn seeds quickly and inexpensively.

Using smart sensors and data analytics tools, predictive maintenance (PdM) aims to reduce downtime by forecasting potential faults; however, accurately distinguishing between failure and normal conditions remains a significant challenge. To address this challenge, we propose a fault classification system that transforms 1D PdM sensor signals into 2D images using a novel Predictive Maintenance Image Transformation (PdM-IT) technique, followed by feature extraction with pre-trained convolutional neural networks (CNNs). The extracted features are then combined across all CNN models. We additionally optimize the combined feature set using three nature-inspired optimization algorithms, including Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), and then evaluate four different classifier such as Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Naive Bayes (NB), and Decision Trees (DT) on the obtained data using cross-validation. In our experimental evaluation, the best-performing model, SVM with features selected via ACO, achieved 98.22% accuracy, 98.39% F1-score, 98.51% precision, and 98.26% recall. These results show the effectiveness of the proposed hybrid image-based fault classification approach and its potential to support more reliable fault diagnosis and operational planning in PdM systems.

Sentimental Analysis (SA) becomes a familiar topic among business people, which is commonly applied for the classification of sentiments from online reviews. It is generally treated as a sentiment classification (SC) problem where the online reviews are categorized into positive or negative polarities using the words that exist in the online reviews. With this motivation, this paper presents a new K-means clustering with hybrid metaheuristic algorithm (KMC-HMA) for SA and classification. The proposed KMC-HMA technique initially performs data preprocessing to remove the unwanted words from the product reviews. In addition, K-means clustering technique is used for the clustering of the massive quantity of the applied product reviews. Moreover, the clustered data are fed into the classification model based on hybrid ant colony optimization (ACO) with dragonfly algorithm (DFA). The ACO algorithm is used for the classification of product reviews and the performance of the ACO algorithm can be optimally tuned by the use of DFA. The performance validation of the KMC-HMA technique is validated using two datasets such as Canon and ipod. The experimental values pointed out the superior performance of the KMC-HMA technique over the recent state of art techniques.

In this paper, we have assessed and investigated the classification accuracies of three different techniques to classify functional near-infrared spectroscopy (fNIRS) signals. Signals were extracted from the motor cortex of the brain using a continuous wave multichannel imaging system. The acquired signals were filtered for noise and converted to oxygenated- and deoxygenated- hemoglobin using modified Beer-Lambert law. From the hemodynamic responses statistical features like slope, mean, skewness, kurtosis, peak and variance were extracted, this was trained on a machine learning classifier giving a classification accuracy of 60.66% for support vector machine (SVM) and 57.22% for k nearest neighbor (KNN), likewise from the hemodynamic response we extracted principal component analysis (PCA) vectors and independent component analysis (ICA) vectors, this along with statistical features were trained on the same SVM and KNN classifier yielding a classification accuracy of 71.4% and 71.8% respectively. Instead of handpicking or handcrafting features, if we let deep learning models, in our case, convolutional neural network (CNN) and long short-term memory (LSTM), choose the features and classify them, they gave a jump of 25% accuracy to over 95% for both architectures.

One of the crucial sectors of Indian livelihood is agriculture. The development and stability of the country have been vastly backed by the farming sector. Thus, ultramodern inventions and technologies can support the testing of new approaches and practices in the farming sector. Artificial intelligence (AI) is one of the most important, useful, and current technologies. Due to its capacity to acquire dependable interpretations from images, Deep learning (DL) in particular has numerous uses. The most common DL architecture for image categorization is Convolutional Neural Networks (CNN). Classifying fruits and vegetables using deep learning is the main emphasis of this work. The model is also compared to some ML classifiers like Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Decision Tree (DT) whereas ResNet the Pretrained Model, Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP) in deep literacy. It was derived that SVM and MLP both showed an accuracy of97.36 on the fruit dataset.

Air pollution is a global environmental problem that poses serious threats to human health and quality of life, especially in industrializing and densely populated areas. The use of machine learning techniques in air pollution prediction has become an important tool in air quality management. In this study, PM10 concentrations were estimated using support vector machines (SVM), artificial neural networks (ANN), convolutional neural networks (CNN), random forest (RF), gradient boosting (GB), K ‐nearest neighbor (KNN), linear regression (Lasso), and long short‐term memory (LSTM) models, and the models were examined in detail. The results showed that the SVM model performed best with the lowest error values (mean absolute error [MAE]: 4.747 µg/m 3 and root mean square error [RMSE]: 6.648 µg/m 3 ), whereas the ANN model achieved the highest accuracy level ( R 2 : 0.791). The KNN model showed the weakest performance with the highest error rates (RMSE: 10.07 µg/m 3 ). Among the LSTM models, the model trained with the Adam optimization algorithm exhibited the best performance compared to other LSTM versions. Among the models used in PM10 estimation, SVM, ANN, and CNN highlight the effectiveness of machine learning models for air pollution estimation and constitute an important reference for studies to be conducted in this field. Focusing on Karaman, a medium‐sized industrial city dominated by food‐processing industries, it provides a representative framework for similar medium‐sized industrial cities facing comparable environmental challenges. The research introduces an integrated and transparent machine‐learning approach that combines data preprocessing, outlier correction, normalization, and broad hyperparameter optimization under consistent conditions for eight algorithms. This framework improves the reliability, comparability, and reproducibility of air‐quality prediction.

The rapid growth of financial technology (fintech) services in Indonesia has significantly transformed digital transaction behavior, with digital wallets such as DANA becoming widely adopted. Despite high usage rates, public discourse on social media frequently highlights both positive experiences and recurring concerns related to system reliability and data security. Twitter, as a real-time public communication platform, provides a rich source of user-generated content that reflects public perception toward fintech services. This study investigates public sentiment toward DANA fintech services in Indonesia through sentiment analysis of Twitter data using machine learning approaches. Purpose:This study aims to analyze public perception of DANA fintech services based on sentiment expressed on Twitter and to compare the performance of several machine learning classification algorithms in identifying positive, neutral, and negative sentiments in Indonesian-language social media texts. Methods/Study design/approach: The study employs a quantitative descriptive approach using text mining and sentiment analysis techniques. Twitter data were collected via the Twitter API over a specified period and processed through preprocessing stages including text cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest algorithms. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Result/Findings:The results indicate that public sentiment toward DANA is predominantly positive, driven by perceptions of transaction convenience and speed, followed by neutral sentiment, while negative sentiment is mainly associated with system disruptions and data security issues. Among the evaluated algorithms, SVM achieved the highest classification accuracy (91.4%), outperforming Random Forest and KNN. These findings demonstrate that SVM is more effective for sentiment classification of short Indonesian-language texts on social media platforms. Novelty/Originality/Value:This study contributes to the growing body of sentiment analysis research in Indonesia by providing empirical evidence on public perception of fintech services using machine learning techniques. By systematically comparing multiple classification algorithms in the context of Indonesian Twitter data, the study offers methodological insights and practical implications for fintech service providers in improving service quality, communication strategies, and user trust.

Social media are the influential Internet community for general netizens to disseminate information, views, and opinions extensively. Recently, the research on Sentiment analysis has intensified due to the vast amount of data obtained from these numerous social networking platforms. During this COVID-19 pandemic, one of the great social networking sites viz. Twitter has experienced a significant increase in online posts and reviews concerning COVID-19 related tweets. In the view of Twitter, the spread of news concerning coronavirus variants has impacted many aspects of the public by providing messages around different issues and opinions to its potential users. In this study, we have used Twitter as a source of data for searching the index keywords and hashtag versions for the terms like “Pandemic”, “Coronavirus”, “COVID-19”, “SARS-CoV-2”, and “Omicron”, and we have examined the psychological and emotional impact it had on the public. This paper provides analytical information about people’s perceptions of coronavirus (tweets were collected from March 2020 to December 2021). With those tweets, sentiment analysis was performed using the TextBlob text processing python module to acquire the people’s subjective data (opinions and feelings) polarity concerning the effects of coronavirus. Furthermore, for effective text classification, we have applied classification methodologies like Support Vector Machine (SVM), K Nearest Neighbor (KNN), Logistic Regression (LR), and Decision Tree (DT). The SVM concerning feature extraction produced the results with 99.49% accuracy. This study assists the government organizations in attaining COVID-19 insights by implying public mental health sentiments in social networks.

Sentiment analysis is the field of study that focuses on finding effectively the conduct of subjective text by analyzing people’s opinions, sentiments, evaluations, attitudes and emotions towards entities. The analysis of data and extracting the opinion word from the data is a challenging task especially when it involves reviews from completely different domains. We perform cross domain sentiment analysis on Amazon product reviews (books, dvd, kitchen appliances, electronics) and TripAdvisor hotel reviews, effectively classify the reviews to positive and negative polarities by applying various preprocessing techniques like Tokenization, POS Tagging, Lemmatization and Stemming which can enhance the performance of sentiment analysis in terms of accuracy and time to train the classifier. Various methods proposed for document-level sentiment classification like Naive Bayes, k-Nearest Neighbor, Support Vector Machines and Decision Tree are analysed in this work. Cross domain sentiment classification is useful because many times we might not have training corpus of specific domains for which we need to classify the data and also cross domain is favoured by lower computation cost and time. Despite poor performance in accuracy, the time consumed for sentiment classification when multiple testing datasets of different domains are present is far less in case of cross domain as compared to single domain. This work aims to define methods to overcome the problem of lower accuracy in cross-domain sentiment classification using different techniques and taking the benefit of being a faster method.

Vehicle classification is an important topic which is still under research consideration because of its role in road surveillance, security system, traffic monitoring, and accident prevention. In this paper, we propose a deep learning model for vehicles classification using the Convolutional Neural Networks (CNN) integrated with a statistical moments layer. We referred to the model as ICNN. As an additional layer, the moments layer extracts statistical moments features from the feature maps obtained from convolutions layers. The moments layer is fed the fully-connected classifier of the network which is fine-tuned to get better results. Our Integrated CNN model (ICNN) achieves 97.1% accuracy compared to the most popular algorithms used in this field such as K Nearest Neighbour (KNN), and Support Vector Machine (SVM), which known as good tools for object classification.

<p class="Normal1">Medicinal plants have been found and utilized in traditional medical practices from ancient times. Many medicinal plants play a vital role in curating many life threatening diseases. Very few of the medicinal herbs are commercially cultivated. Many plant diseases are there which destroys these medicinal plants. Early detection of plant diseases can prevent the huge loss of these medicinal plants. Here, we presented a hybrid model that makes use of SVM along with the traditional convolutional neural network (CNN) for predicting <em>Basil</em> plants leaves diseases. We transformed the conventional CNN model by adding a classification layer Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) after feature extraction and this approach tends to perform better than traditional CNN as we make the dataset balanced by data augmentation and SVN and KNN tend to perform better in case of balanced samples. CNN is used for training, SVM/KNN is used for classification. The advantages of CNN and SVM are used in proposed the CNN and SVM and KNN model. It is assumed that such a combined model would incorporate the benefits of CNN and SVM. Here, we identified the four types of diseases that affect <em>basil</em> plant leaves as Leaf spot, Downy mildew, Fusarium wilt, Fungal, and Healthy. Since there isn’t a standard dataset for <em>basil</em> leaves, we created our own 803 picture data set and used various machine learning techniques to train and evaluate the model. However, over other existing algorithms, our hybrid model i.e., CNN+SVM has produced more accurate results. For five classes of <em>basil</em> plant leaves, the proposed model produced 95.02% accuracy of for leaf diseases.</p>