scholarly journals Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1645
Author(s):  
Ishani Chatterjee ◽  
Mengchu Zhou ◽  
Abdullah Abusorrah ◽  
Khaled Sedraoui ◽  
Ahmed Alabdulwahab
Keyword(s):  
Anomaly Detection ◽  
Sentiment Analysis ◽  
Outlier Detection ◽  
State Of The Art ◽  
Correction Method ◽  
Machine Learning Techniques ◽  
True Nature ◽  
Analysis Algorithm ◽  
Star Rating ◽  
Customer Reviews

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.

Sentiment Recognition in Customer Reviews Using Deep Learning

2018 ◽  
Vol 14 (2) ◽  
pp. 77-86 ◽  
Author(s):  
Vinay Kumar Jain ◽  
Shishir Kumar ◽  
Prabhat Mahanti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Customer Reviews ◽  
Analysis Question ◽  
Comparison Of The Results

Deep learning has become popular in all aspect related to human judgments. Most machine learning techniques work well which includes text classification, text sequence learning, sentiment analysis, question-answer engine, etc. This paper has been focused on two objectives, firstly is to study the applicability of deep neural networks strategies for extracting sentiment present in social media data and customer reviews with effective training solutions. The second objective is to design deep networks that can be trained with these weakly supervised strategies in order to predict meaningful inferences. This paper presents the concept and steps of using deep learning for extraction sentiments from customer reviews. The extraction pulls out the features from the customer reviews using deep learning popular methods including Convolution neural networks (CNN) and Long Short-Term Memory (LSTM) architectures. The comparison of the results with tradition text classification method such as Naive Bayes(NB) and Support Vector Machine(SVM) using two data sets IMDB reviews and Amazon customer reviews have been presented. This work mainly focused on investigating the merit of using deep models for sentiment analysis in customer reviews.

scholarly journals Sentiment Analysis of Movie Review using Machine Learning Techniques

2018 ◽  
Vol 7 (2.7) ◽  
pp. 676 ◽  
Author(s):  
V Uma Ramya ◽  
K Thirupathi Rao
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Naive Bayes ◽  
Multinomial Logistic Regression ◽  
Machine Learning Techniques ◽  
Analysis Algorithm ◽  
Learning Techniques ◽  
Almost All

Today's online world was fully filled up with blogs, views, comments, posts through various websites and social-surfs. People were habituated with posting every incident into blogs, messed with comments like text and emotions, which are a mixed bag of sad, happy, worry, cry etc. Analysing such data was called as Sentimental Analysis. To analysis, these unordered data we use new emerged technology algorithms. Machine learning a transpire technology which is engaged with almost all the fields, where its algorithms are more powerful that give with better faultless results. In this paper, we are analyzing tweets based on movie reviews using the Multinomial Logistic Regression, Naïve Bayes, and SVM algorithms to compare score value to show the best text analysis algorithm. 

Sentiment Analysis in Hindi—A Survey on the State-of-the-art Techniques

2022 ◽  
Vol 21 (1) ◽  
pp. 1-46
Author(s):  
Dhanashree S. Kulkarni ◽  
Sunil S. Rodd
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Relevant Information ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Indian Languages ◽  
Lexical Resources ◽  
Comprehensive Overview ◽  
Hybrid Techniques ◽  
Learning Techniques

Sentiment Analysis (SA) has been a core interest in the field of text mining research, dealing with computational processing of sentiments, views, and subjective nature of the text. Due to the availability of extensive web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. It has become extremely significant to analyze this data and recover valuable and relevant information. Hindi being the first language of the majority of the population in India, SA in Hindi has turned out to be a critical task particularly for companies and government organizations. This research portrays a systematic review specifically in the field of Hindi SA. The major contribution of this article includes the categorization of numerous articles based on techniques that have attracted researchers in performing SA tasks in Hindi language. This survey classifies these state-of-the-art computational intelligence techniques into four major categories namely lexicon-based techniques, machine learning techniques, deep learning techniques, and hybrid techniques. It discusses the importance of these techniques based on different aspects such as their impact on the issues of SA, levels of analysis, and performance evaluation measures. The research puts forward a comprehensive overview of the majority of the work done in Hindi SA. This study will help researchers in finding out resources such as annotated datasets, linguistic resources, and lexical resources. This survey delivers some significant findings and presents overall future research directions in the field of Hindi SA.

Sentiment Recognition in Customer Reviews Using Deep Learning

2020 ◽  
pp. 1383-1393
Author(s):  
Vinay Kumar Jain ◽  
Shishir Kumar ◽  
Prabhat Kumar Mahanti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Customer Reviews ◽  
Weakly Supervised ◽  
Comparison Of The Results

Deep learning has become popular in all aspect related to human judgments. Most machine learning techniques work well which includes text classification, text sequence learning, sentiment analysis, question-answer engine, etc. This paper has been focused on two objectives, firstly is to study the applicability of deep neural networks strategies for extracting sentiment present in social media data and customer reviews with effective training solutions. The second objective is to design deep networks that can be trained with these weakly supervised strategies in order to predict meaningful inferences. This paper presents the concept and steps of using deep learning for extraction sentiments from customer reviews. The extraction pulls out the features from the customer reviews using deep learning popular methods including Convolution neural networks (CNN) and Long Short-Term Memory (LSTM) architectures. The comparison of the results with tradition text classification method such as Naive Bayes(NB) and Support Vector Machine(SVM) using two data sets IMDB reviews and Amazon customer reviews have been presented. This work mainly focused on investigating the merit of using deep models for sentiment analysis in customer reviews.

scholarly journals A Comprehensive Survey on Aspect Based Word Embedding Models and Sentiment Analysis Classification Approaches

2021 ◽  
Author(s):  
Monika Agrawal ◽  
Nageswara Rao Moparthi
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Sentiment Classification ◽  
Word Embedding ◽  
Machine Learning Techniques ◽  
Customer Reviews ◽  
Aspect Extraction ◽  
Learning Techniques ◽  
Entire Sentence ◽  
Comprehensive Survey

Sentiment Analysis includes methods and techniques for businesses to understand and analyze customer reviews, feedback and opinion on a particular product or service. Sentiment Analysis uses Natural Language Processing (NLP) tools to analyze feelings or emotions, attitudes, opinions, thoughts, etc. behind the words. Sentiments such as positive, negative and neutral are associated with a particular product. Sentiment analysis is applicable in multi-domains such as customer feedback for a particular product, movie reviews, social and political comments. This survey basically focuses on different aspect-based word embedding models and aspect-based sentiment classification techniques, where the goal is to extract key features from the sentences and classify sentiment on entities at document level. Aspect Based Sentiment Analysis (ABSA) is a technique that concentrates not only the entire sentence but analyses key terms explicitly to predict the polarity as a whole. ABSA model accepts aspect categories and its corresponding aspect terms to generate sentiment corresponding to each aspect from the text corpus. This article provides a comprehensive survey on different word embedding models under CNN framework for aspect extraction and different machine learning techniques applicable for sentiment classification purpose.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

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