scholarly journals Tourism web app with Aspect Based Sentiment Classification Framework for Tourist Review

2021 ◽  
Vol 9 (11) ◽  
pp. 1452-1455
Author(s):  
Prof S. S. Khartad
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
High Accuracy ◽  
Sentiment Classification ◽  
Sources Of Information ◽  
Tourist Destinations ◽  
Classification Framework ◽  
Classification Tasks ◽  
Web App ◽  
Real World Datasets

Abstract: According to studies, current tourism recommendation systems make false recommendations that do not live up to tourist expectations. Among The majority of these systems are inefficient, which is one of the main causes of the problem. A recommendation system that incorporates user feedback element.Tourist reviews are sources of information for travellers interested in learning more about tourist destinations. Regrettably, some reviews are irrelevant, resulting in noisy statistics. Sentiment categorization algorithms based on aspects have showed potential in reducing noise. We proposed a framework for sentiment classification based on aspects that can not only detect aspects quickly but also execute classification tasks with high accuracy. The framework has been deployed to assists travellers in finding the best restaurant or lodging in a city, and its performance has been evaluated with outstanding results using real-world datasets. Keywords: Pre-processing, Classifier algorithm, Feature extraction NLP, Tourism Strategy,Machine Learning, Tourist Reviews, Aspect Based Sentiment Analysis etc.

scholarly journals Survey Paper on Recommendation System for Tourist Reviews using Aspect Based Sentiment Classification

2021 ◽  
Vol 9 (VIII) ◽  
pp. 821-824
Author(s):  
Kande Trupti V
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Web Sites ◽  
Recommendation System ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Classification Methods ◽  
Survey Paper ◽  
Tourism Sector

The tourism and travel sector is trying to provide different facility using a large amount of data collected from different tourism web sites. The tourist easily retrieves to reviews, evidence of different tourists and accesses them properly. It helps tourists have made the planning of visit to tourism place. So that, a major challenge faced by tourism sector is to utilize the accumulate information for detecting tourist preferences. Unfortunately, some user's comments are irrelevant and complex for understanding and long-winded these become hard for recommendation. Aspect based sentiment classification methods have shown promise in overcome the issue. In existing not much work on aspect based sentiment with classification. Here in this paper represents a framework of aspect based sentiment classification recommendation system that will not only identify the aspects very efficiently but can perform classification task with high accuracy using machine learning algorithms. This framework helps tourists to find better tourist spot, hotels, restaurant and resort in a city, and here performance has been evaluated by conducting experiments on Yelp and foursquare real-time datasets.

scholarly journals Supervised Learning Using Homology Stable Rank Kernels

2021 ◽  
Vol 7 ◽  
Author(s):  
Jens Agerberg ◽  
Ryan Ramanujam ◽  
Martina Scolamiero ◽  
Wojciech Chachólski
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real World ◽  
Fundamental Property ◽  
Stable Rank ◽  
Topological Data Analysis ◽  
Improve Accuracy ◽  
Recent Developments ◽  
Classification Tasks ◽  
Real World Datasets

Exciting recent developments in Topological Data Analysis have aimed at combining homology-based invariants with Machine Learning. In this article, we use hierarchical stabilization to bridge between persistence and kernel-based methods by introducing the so-called stable rank kernels. A fundamental property of the stable rank kernels is that they depend on metrics to compare persistence modules. We illustrate their use on artificial and real-world datasets and show that by varying the metric we can improve accuracy in classification tasks.

scholarly journals A Novel Ant Colony Based DBN Framework to Analyze the Drug Reviews

2021 ◽  
Vol 13 (6) ◽  
pp. 25-39
Author(s):  
Nazia Tazeen ◽  
◽  
K. Sandhya Rani
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fitness Function ◽  
Computation Time ◽  
High Accuracy ◽  
Sentiment Classification ◽  
Big Data Applications ◽  
Classification Tasks ◽  
Research Drug ◽  
Drug Review

Nowadays, big data is directing the entire advanced world with its function and applications. Moreover, to make better decisions from the ever emerging big data belonging to the respective organizations, deep learning (DL) models are required. DL is also widely used in the sentiment classification tasks considering data from social networks.Furthermore, sentiment classification signifies the best way to analyze the big data and make decisions accordingly. Analyzing the sentiments from big data applications is quite challenging task and also requires more time for the execution process. Therefore, to analyze and classify big data emerging from social networks in a better way, DL models are utilized. DL techniques are being used among the researchers to get high end results. A novel Ant Colonybased Deep Belief Neural Network (AC-DBN) framework is proposed in this research. Drug review tweets are opted to perform sentiment classification by using the proposed framework in python environment. A model fitness function is initiated in the DL framework and is observed that it is attaining high accuracy with low computation time. Additionally, the obtained results attained from the proposed framework are validated with existing methods for evaluating the efficiency of the proposed AC-DBN approach.

scholarly journals Optimization of Feature Set for Sentiment Analysis using Artificial Butterfly Algorithm and Ensemble Machine Learning

2020 ◽  
Vol 6 (11) ◽  
pp. 21-27
Author(s):  
Jyoti Hanvat ◽  
Sumit Sharma
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Sentiment Classification ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Essential Components ◽  
Classification Tasks ◽  
High Degree

The current decade has witnessed the remarkable developments in the field of artificial intelligence, and the revolution of deep learning has transformed the whole artificial intelligence industry. Eventually, deep learning techniques have become essential components of any model in today’s computational world. Nevertheless, ensemble learning techniques promise a high degree of automation with generalized rule extraction for both text and sentiment classification tasks. This paper aims designed and implemented optimized feature matrix using ensemble learning used for sentiment classification and its applications.

Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation

2021 ◽  
Vol 25 (4) ◽  
pp. 1013-1029
Author(s):  
Zeeshan Zeeshan ◽  
Qurat ul Ain ◽  
Uzair Aslam Bhatti ◽  
Waqar Hussain Memon ◽  
Sajid Ali ◽  
...  
Keyword(s):  
Recommendation System ◽  
Sources Of Information ◽  
Multiple Features ◽  
Recommendation Algorithms ◽  
Label Correlations ◽  
Feature Based ◽  
Multicriteria Model ◽  
Implicit And Explicit ◽  
Hybrid Recommendation ◽  
Different Sources

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.

scholarly journals Selection of Suitable Machine Learning Algorithms for Classification Tasks in Reverse Logistics

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 272-277
Author(s):  
Hannah Lickert ◽  
Aleksandra Wewer ◽  
Sören Dittmann ◽  
Pinar Bilge ◽  
Franz Dietrich
Keyword(s):  
Machine Learning ◽  
Reverse Logistics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Tasks ◽  
Selection Of

scholarly journals Estimating Causal Effects When the Treatment Affects All Subjects Simultaneously: An Application

2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Chiara Binelli
Keyword(s):  
Machine Learning ◽  
Carbon Dioxide ◽  
Global Warming ◽  
Carbon Dioxide Emissions ◽  
Control Group ◽  
Sources Of Information ◽  
Average Global Temperature ◽  
Causal Impact ◽  
The Impact ◽  
Robust Evidence

Several important questions cannot be answered with the standard toolkit of causal inference since all subjects are treated for a given period and thus there is no control group. One example of this type of questions is the impact of carbon dioxide emissions on global warming. In this paper, we address this question using a machine learning method, which allows estimating causal impacts in settings when a randomized experiment is not feasible. We discuss the conditions under which this method can identify a causal impact, and we find that carbon dioxide emissions are responsible for an increase in average global temperature of about 0.3 degrees Celsius between 1961 and 2011. We offer two main contributions. First, we provide one additional application of Machine Learning to answer causal questions of policy relevance. Second, by applying a methodology that relies on few directly testable assumptions and is easy to replicate, we provide robust evidence of the man-made nature of global warming, which could reduce incentives to turn to biased sources of information that fuels climate change skepticism.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

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