scholarly journals Aspect Based Sentiment Analysis for E-Commerce Websites with Visualization through Machine Learning Algorithm

E-commerce is evolving at a rapid pace that new doors have been opened for the people to express their emotions towards the products. The opinions of the customers plays an important role in the e-commerce sites. It is practically a tedious job to analyze the opinions of users and form a pros and cons for respective products. This paper develops a solution through machine learning algorithms by pre-processing the reviews based on features of mobile products. This mainly focus on aspect level of opinions which uses SentiWordNet, Natural Language Processing and aggregate scores for analyzing the text reviews. The experimental results provide the visual representation of products which provide better understanding of product reviews rather than reading through long textual reviews which includes strengths and weakness of the product using Naive Bayes algorithm. This results also helps the e-commerce vendors to overcome the weakness of the products and meet the customer expectations.

2020 ◽  
Vol 7 (10) ◽  
pp. 380-389
Author(s):  
Asogwa D.C ◽  
Anigbogu S.O ◽  
Anigbogu G.N ◽  
Efozia F.N

Author's age prediction is the task of determining the author's age by studying the texts written by them. The prediction of author’s age can be enlightening about the different trends, opinions social and political views of an age group. Marketers always use this to encourage a product or a service to an age group following their conveyed interests and opinions. Methodologies in natural language processing have made it possible to predict author’s age from text by examining the variation of linguistic characteristics. Also, many machine learning algorithms have been used in author’s age prediction. However, in social networks, computational linguists are challenged with numerous issues just as machine learning techniques are performance driven with its own challenges in realistic scenarios. This work developed a model that can predict author's age from text with a machine learning algorithm (Naïve Bayes) using three types of features namely, content based, style based and topic based. The trained model gave a prediction accuracy of 80%.


2019 ◽  
Vol 16 (10) ◽  
pp. 4425-4430 ◽  
Author(s):  
Devendra Prasad ◽  
Sandip Kumar Goyal ◽  
Avinash Sharma ◽  
Amit Bindal ◽  
Virendra Singh Kushwah

Machine Learning is a growing area in computer science in today’s era. This article is focusing on prediction analysis using K-Nearest Neighbors (KNN) Machine Learning algorithm. Data in the dataset are processed, analyzed and predicated using the specified algorithm. Introduction of various Machine Learning algorithms, its pros and cons have been discussed. The KNN algorithm with detail study is given and it is implemented on the specified data with certain parameters. The research work elucidates prediction analysis and explicates the prediction of quality of restaurants.


2020 ◽  
Vol 17 (9) ◽  
pp. 4294-4298
Author(s):  
B. R. Sunil Kumar ◽  
B. S. Siddhartha ◽  
S. N. Shwetha ◽  
K. Arpitha

This paper intends to use distinct machine learning algorithms and exploring its multi-features. The primary advantage of machine learning is, a machine learning algorithm can predict its work automatically by learning what to do with information. This paper reveals the concept of machine learning and its algorithms which can be used for different applications such as health care, sentiment analysis and many more. Sometimes the programmers will get confused which algorithm to apply for their applications. This paper provides an idea related to the algorithm used on the basis of how accurately it fits. Based on the collected data, one of the algorithms can be selected based upon its pros and cons. By considering the data set, the base model is developed, trained and tested. Then the trained model is ready for prediction and can be deployed on the basis of feasibility.


Author(s):  
Charu Latkar

For the protection and proximity of railway networks it is substantial to Promptly detect and identify faults in the railway tracks. In this paper, railway track fault diagnosis is approximated from the vertical and lateral acceleration using a MPU6050. MPU6050 consisting of three sensors namely gyroscope, magnetometer and accelerometer are used to distinguish line and level as symetricities in a railway track. A GSM module is used to notify the location of faults on tracks. Arduino Microcontroller is interfaced using Arduino UNO IDE. The results show that the condition of railway track irregularity and railway track striation can be approximated constructively. The processed data is uploaded to the open source cloud provider thingspeak.com. The use of various Machine Learning Algorithms are proposed to accomplish the above tasks based on the commonly available measured signals. By considering the signals from multiple railway tracks in a geographic location, faults are diagnosed from their spatial and temporal dependencies. The irregularities in the railway tracks are detected using the Inertial Monitoring Unit, providing the necessary data about future deformities using Machine Learning. Using Python 3.0, a generative model is developed to show that the AdaBoost network can learn these dependencies directly from the data. Seven different classification algorithms used for this project are Logistic regression,Naive Bayes Algorithm,Support Vector Machine, Ensemble Machine (Average) learning Algorithm, XGBoost Classifier, Extreme Machine Learning and AdaBoost Classifier. Among the above 7 classification algorithms, AdaBoost Learning has given the highest accuracy,i.e of 93.93 %. The AdaBoost Machine Learning Model is used throughout the model.


The process of discovering and analyzing the customer feedback using Natural Language Processing (NLP) is said to be sentiment analysis. Based on the surge over the concept of rating level in sentiment analysis, sentiment is utilized as an attribute for certain aspects or features that get expressed and more attention are provided to the problem of detecting the customer reviews. Despite the wide use and popularity of some methods, a better technique for identifying the polarity of a text data is hard to find. Machine learning has recently attracted attention as an approach for sentiment analysis. This work extends the idea of evaluating the performance of various Machine Learning (ML) classifiers namely logistic regression, Naive Bayes, Support Vector Machine (SVM) and Neural Network (NN).To show their effectiveness in sentiment mining of customer product reviews, the customer feedback has been collected from Grocery and Gourmet Food. Nearly 90 thousands customers feedback reviews of various product related categories namely Product ID, rating, review test, review time reviewer ID and reviewer name are used in this analysis. The performance of the classifiers is measured in terms of accuracy, specificity and sensitivity. From the experimental results, the better machine learning classification algorithm is proposed for sentiment mining using online shopping customer review data.


Author(s):  
Saurabh Singh

Twitter sentiment analysis is the method of Natural Language Processing (NLP). In this project named Twitter sentiment Analysis we analyze the sentiments behind the twitter’s tweet. We have three type of sentiment: Positive, Neutral and Negative. Analyzing the sentiments behind every tweet is the biggest problem in the early days but now it can be solved with the help of Machine Learning. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters and through the Twitter Sentimental Analysis we can analysis the mood of the person who tweet which can helps in the industries to analyze the market and their product reviews or we can know the sentiments behind the opinion on any topic on which the group of people tweet and through this we can find the final result that the people point on view on the particular topic, product and any other tweets suggestions.


2011 ◽  
Vol 17 (4) ◽  
pp. 541-567 ◽  
Author(s):  
M. SOKOLOVA ◽  
G. LAPALME

AbstractThe user-generated Web content has been intensively analyzed in Information Extraction and Natural Language Processing research. Web-posted reviews of consumer goods are studied to find customer opinions about the products. We hypothesize that nonemotionally charged descriptions can be applied to predict those opinions. The descriptions may include indicators of product size (tall), commonplace (some), frequency of happening (often), and reviewer certainty (maybe). We first construct patterns of how the descriptions are used in consumer-written texts and then represent individual reviews through these patterns. We propose a semantic hierarchy that organizes individual words into opinion types. We run machine learning algorithms on five data sets of user-written product reviews: four are used in classification experiments, another one for regression and classification. The obtained results support the use of non-emotional descriptions in opinion learning.


Author(s):  
P. Ajitha ◽  
A. Sivasangari ◽  
R. Immanuel Rajkumar ◽  
S. Poonguzhali

Text Sentiment Analysis is a system where text feeling polarity is positive or negative or neutral from a series of texts or documents or public opinions on a particular product or general subject. Using machine learning and natural language processing techniques, the current work aims to gain insight into sentiment mining on tweets. Text classification is accomplished using Machine Learning Algorithm-based fusion technique. This research suggested a system for grading feelings based on a lexicon. Bag-of-words (BOW) or lexicon-based methodology is currently the main standard way of modeling text for machine learning in sentiment analysis approaches. Marketers can use sentiment analysis to analyze their business and services, public opinion, or to evaluate customer satisfaction. Organizations can even use this analysis to gather significant feedback on issues related to newly released products. The main objective of this is to resolve the data overload problem.


2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.


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