A machine learning approach to analyze customer satisfaction from airline tweets
TL;DR: This study presents a machine learning approach to analyze the tweets to improve the customer’s experience and found that convolutional neural network (CNN) outperformed SVM and ANN models.
Abstract: Customer’s experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study presents a machine learning approach to analyze the tweets to improve the customer’s experience. Features were extracted from the tweets using word embedding with Glove dictionary approach and n-gram approach. Further, SVM (support vector machine) and several ANN (artificial neural network) architectures were considered to develop classification model that maps the tweet into positive and negative category. Additionally, convolutional neural network (CNN) were developed to classify the tweets and the results were compared with the most accurate model among SVM and several ANN architectures. It was found that CNN outperformed SVM and ANN models. In the end, association rule mining have been performed on different categories of tweets to map the relationship with sentiment categories. The results show that interesting associations were identified that certainly helps the airline industries to improve their customer’s experience.
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TL;DR: In this paper, the authors present a study to determine the usefulness, scope, and applicability of this alliance of ML techniques for consumer sentiment analysis (CSA) for online reviews in the domain of hospitality and tourism.
68 citations
Posted Content•
TL;DR: The study is presented to find out the usefulness, scope, and applicability of this alliance of Machine Learning techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism.
Abstract: Consumer sentiment analysis is a recent fad for social media related applications such as healthcare, crime, finance, travel, and academics. Disentangling consumer perception to gain insight into the desired objective and reviews is significant. With the advancement of technology, a massive amount of social web-data increasing in terms of volume, subjectivity, and heterogeneity, becomes challenging to process it manually. Machine learning techniques have been utilized to handle this difficulty in real-life applications. This paper presents the study to find out the usefulness, scope, and applicability of this alliance of Machine Learning techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism. We have shown a systematic literature review to compare, analyze, explore, and understand the attempts and direction in a proper way to find research gaps to illustrating the future scope of this pairing. This work is contributing to the extant literature in two ways; firstly, the primary objective is to read and analyze the use of machine learning techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism. Secondly, in this work, we presented a systematic approach to identify, collect observational evidence, results from the analysis, and assimilate observations of all related high-quality research to address particular research queries referring to the described research area.
66 citations
TL;DR: The proposed system, namely Senti‐eSystem, aims at the development of sentiment‐based eSystem using hybridized Fuzzy and Deep Neural Network for Measuring Customer Satisfaction to assist business organizations for improving the quality of their services and products.
Abstract: In the competing era of online industries, understanding customer feedback and satisfaction is one of the important concern for any business organization. The well‐known social media platforms like Twitter are a place where customers share their feedbacks. Analyzing customer feedback is beneficial, as it provides an advantage way of unveiling customer interests. The proposed system, namely Senti‐eSystem, aims at the development of sentiment‐based eSystem using hybridized Fuzzy and Deep Neural Network for Measuring Customer Satisfaction to assist business organizations for improving the quality of their services and products. The proposed approach initially deploys a Bidirectional Long Short Term Memory with attention mechanism to predict the sentiment polarity that is positive and negative, followed by Fuzzy logic approach to determine the customer satisfaction level, which further strengthens the capabilities of the proposed approach. The system achieves an accuracy of 92.86%, outperforming the previous state‐of‐art lexicon‐based approaches. Moreover, the effectiveness of the proposed system is also validated by applying the statistical test.
62 citations
Cites methods from "A machine learning approach to anal..."
...texts_to_sequences: To convert the given review into an array of indexes that is, [1, 2, 3, 4, 5], which is then passed to the embedding layer of the DL model....
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TL;DR: A systematic and structured literature review of the feature-selection techniques used in studies related to big genomic data analytics and how it contributes to the research community is presented.
Abstract: In the era of accelerating growth of genomic data, feature-selection techniques are believed to become a game changer that can help substantially reduce the complexity of the data, thus making it easier to analyze and translate it into useful information. It is expected that within the next decade, researchers will head towards analyzing the genomes of all living creatures making genomics the main generator of data. Feature selection techniques are believed to become a game changer that can help substantially reduce the complexity of genomic data, thus making it easier to analyze it and translating it into useful information. With the absence of a thorough investigation of the field, it is almost impossible for researchers to get an idea of how their work relates to existing studies as well as how it contributes to the research community. In this paper, we present a systematic and structured literature review of the feature-selection techniques used in studies related to big genomic data analytics.
57 citations
21 Jul 2021
TL;DR: A hybrid convolutional neural network-long short-term memory (CNN-LSTM) model is proposed for sentiment analysis, which demonstrates that the proposed model outperforms with 91.3% accuracy in sentiment analysis.
Abstract: With the fastest growth of information and communication technology (ICT), the availability of web content on social media platforms is increasing day by day. Sentiment analysis from online reviews drawing researchers’ attention from various organizations such as academics, government, and private industries. Sentiment analysis has been a hot research topic in Machine Learning (ML) and Natural Language Processing (NLP). Currently, Deep Learning (DL) techniques are implemented in sentiment analysis to get excellent results. This study proposed a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for sentiment analysis. Our proposed model is being applied with dropout, max pooling, and batch normalization to get results. Experimental analysis carried out on Airlinequality and Twitter airline sentiment datasets. We employed the Keras word embedding approach, which converts texts into vectors of numeric values, where similar words have small vector distances between them. We calculated various parameters, such as accuracy, precision, recall, and F1-measure, to measure the model’s performance. These parameters for the proposed model are better than the classical ML models in sentiment analysis. Our results analysis demonstrates that the proposed model outperforms with 91.3% accuracy in sentiment analysis.
51 citations
References
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01 Oct 2014
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Abstract: Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.
30,558 citations
Book•
08 Sep 2000TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data
23,600 citations
TL;DR: WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.
Abstract: Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].
15,068 citations
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Abstract: A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.
12,443 citations
Proceedings Article•
12 Sep 1994
10,454 citations