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Sentiment Analysis is a method of extracting useful insight from text or expressions that help make decisions for different fields like establishing a new business, purchasing electronic products, or overall community analysis. Different techniques for sentiment analysis have been used in various researches. This research used a machine learning cl...
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... are categorized as "Positive" or "negative," or they may suggest some right decision [8,14]. Sentiment Analysis methods involve the following series of steps: Figure 2 describes the detailed process of sentiment analysis from tweets extraction, feature generation from these tweets to final result finding as positive or negative opinion. These steps are described in the following section in detail. ...
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Sentiment Analysis is a method of extracting useful insight from text or expressions that help make decisions for different fields like establishing a new business, purchasing electronic products, or overall community analysis. Different techniques for sentiment analysis have been used in various researches. This research used a machine learning cl...
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... Precision is calculated by determining the ratio of accurate predictions to the total number of predictions made. [49]. It means precision is based on the test data set. ...
Internet of Things (IoT) assisted application in agriculture shows tremendous success to improve productivity in agriculture. Agriculture is grappling with issues such as depleted soil fertility, climate-related hazards like intensified pest attacks and diseases. Accurate forecasting of pest outbreaks can play a vital role in improving agricultural yield. Utilizing IoT technology for environmental monitoring in crop fields to forecast pest attacks. The important parameters for pest predictions are temperature, humidity, rainfall, wind speed and sunshine duration. Directly sensed environmental conditions are utilized as input to a deep learning model, which makes binary decisions about the presence of pest populations based on the prevailing environmental conditions. The accuracy and precision of the deep learning model in making predictions are assessed through evaluation with test data. Five year data 2028 to 2022 have been used for making prediction. The model of pest prediction generates weekly predictions. The overall accuracy of the weekly predictions is 94% and high F-measure, Precision, Recall, Cohens kappa, and ROC AUC for making to optimize the prediction. The accuracy of the pest prediction improves gradually with time. Weekly predictions are generated from the means of all environment conditions from the last seven days. The weekly predictions are important for the short-term measures against the pest attacks.
... Nowadays, computational technologies are being used in various domains of life, including healthcare [14], security [15] [21] [25] and also in safety purposes [16], disaster [17], and situational awareness [19] [26] [27] in the educational domain [18] as well. Sentiment analysis is a prominent research topic in demand under the category of NLP [20]. Another method has been used to forecast human emotions reflected in product reviews taken from Amazon [2]. ...
The exponential growth in online reviews and recommendations availability drives sentiment classification, an interesting topic in industrial research. There is a vital requirement for organizations to explore client behaviour to assess the competitive business environment. This study aspires to examine and predict customer reviews using Transfer learning (TL) approaches. Reviews can span so many domains that it is challenging to gather annotated training data for all of them. Hence, this paper proposed an annotation algorithm to label a large unlabeled dataset. These reviews must be pulled and examined to predict the sentiment polarity, whether the review is positive, neutral, or negative. We propose a deep learning-based approach that learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classifiers trained with this high-level feature representation outperform state-of-the-art methods on a benchmark of reviews of cosmetics brands on Amazon or other platforms. Using the BERT for sentiment analysis, we achieved the highest accuracy of 93.21% compared to previous studies. Index Terms-Cosmetic purchase behaviour, Text mining, Sentiment analysis.
... The methodology uses machine learning algorithms like Multinomial Naive Bayes (MNB), Logistic Regression (LR), Support Vector Machine (SVM), and k-nearest neighbor (k-NN). Nowadays, computational technologies are being used in various domains of life, including healthcare [25], security and also in safety purposes, disaster and situational awareness [26] in the educational domain [27] as well. ...
... The methodology uses machine learning algorithms like Multinomial Naive Bayes (MNB), Logistic Regression (LR), Support Vector Machine (SVM), and k-nearest neighbor (k-NN). Nowadays, computational technologies are being used in various domains of life, including healthcare [25], security and also in safety purposes, disaster and situational awareness [26] in the educational domain [27] as well. ...
Google Play Store is a popular distribution channel with millions of applications. WhatsApp is the most downloaded communication application on Play Store. A few months ago, WhatsApp changed its privacy policy, triggering a wave of user reviews outrage. Privacy is essential in the application; users are worried about their data security and privacy. A computational system must be required to analyze the user’s reviews for WhatsApp authority to make better policies. This study aims to develop a deep learning-based model for automatically assessing reviews that can be adapted for future data analysis. We proposed a deep learning methodology by using Aspect-based sentiment analysis (ABSA) utilizing the communication app reviews scraped from the Google play store using the Google Play scrapper application. This study uses the text mining technique for ABSA on the user’s reviews. For Topic extraction, we have used Latent Dirichlet Allocation (LDA) and the deep learning method Long Short-Term Memory (LSTM) for topic classification. The results show that our proposed model gives us a promising outcome with 90% accuracy by using the LSTM model. WhatsApp authority can use the results to optimize communication applications by adding more efficient features and updating them.
... Currently, every well-known organization uses data mining and machine learning applications frequently to explore the meaningful hidden patterns from raw collected data [17][18][19]. Nowadays, machine learning is used in various aspects of life [20] to perform complex analyses and explore hidden patterns from data points [21]. The authors of [5] calculated the distance between each unique landmark and a nose peak to capture the changes. ...
... They compared their performances with state-of-the-art methods. Nowadays, computational technologies are being used in various domains of life, including healthcare [24], security [23] and also in safety purposes [21], and disaster [20,23] as well. ...
Human convey their message in different forms. Expressing their emotions and moods through their facial expression is one of them. In this work, to avoid the traditional process of feature extraction (Geometry based method, Template based method, and Appearance based method), CNN model is used as a feature extractor for emotion detection using facial expression. In this study we also used three pre-trained models VGG-16, ResNet-50, Inception-V3. This Experiment is done on Fer-2013 facial expression dataset and Cohn Extended (CK+) dataset. By using FER-2013 dataset the accuracy rates for CNN, ResNet-50, VGG-16 and Inception-V3 are 76.74%, 85.71%, 85.78%s, 97.93% respectively. Similarly, the experimental results using CK+ dataset showed the accuracy rates for CNN, ResNet- 50, VGG-16 and Inception-V3 are 84.18%, 92.91%, 91.07%, and 73.16% respectively. The experimental results showed exceptional results for Inception-V3 with 97.93% using FER-2013 dataset and ResNet-50 with 91.92% using CK+ dataset.
