Sentimental Analysis for Airline Twitter data

Das, Deb Dutta; Sharma, Sharan Prakash; Natani, Shubham; Khare, Neelu; Singh, Brijendra

doi:10.1088/1757-899x/263/4/042067

Cited by 26 publications

(11 citation statements)

References 2 publications

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“…Using the training data and the features of the new instance, NB calculates its likelihood to belong to a class. NB was employed to classify the related posts to transportation or events by (Abalı et al, 2018), to analyse commuters' sentiment toward transportation by (Alamsyah et al, 2018;Dutta Das et al, 2017;Fiarni et al, 2018;Kumar et al, 2014;Liyang et al, 2016;Sternberg et al, 2018) and to predict vehicle recall by (X. Zhang et al, 2015).…”

Section: S6-rq1: What Are the Datasets Used By Researchers?mentioning

confidence: 99%

“…Zhang, Zhang, et al, 2018;Zhang, Chen, et al, 2018) used multiple classifiers to compare their results in analysing commuters' sentiment toward transportation related topics, while (D'Andrea et al, 2015;Gal-Tzur et al, 2018;Hoang et al, 2016;Kuflik et al, 2017;Tse et al, 2016) used different machine learning techniques to identify the posts related to a transportation topic. Other classification approaches such as Maximum Entropy (ME) (Dutta Das et al, 2017;Samonte et al, 2018) and Logistic Regression (LR) (Rane & Kumar, 2018;Zhang, Chen, et al, 2018; were also used by some researchers. ME selects the appropriate distribution to represent the data based on the measurement of entropy, whereas LR is used to represent the data and describe the relationship between variables.…”

Section: S6-rq1: What Are the Datasets Used By Researchers?mentioning

confidence: 99%

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Investigating transportation research based on social media analysis: a systematic mapping review

et al. 2021

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Social media is a pool of users' thoughts, opinions, surrounding environment, situation and others. This pool can be used as a real-time and feedback data source for many domains such as transportation. It can be used to get instant feedback from commuters; their opinions toward the transportation network and their complaints, in addition to the traffic situation, road conditions, events detection and many others. The problem is in how to utilize social media data to achieve one or more of these targets. A systematic review was conducted in the field of transportation-related research based on social media analysis (TRR-SMA) from the years between 2008 and 2018; 74 papers were identified from an initial set of 703 papers extracted from 4 digital libraries. This review will structure the field and give an overview based on the following grounds: activity, keywords, approaches, social media data and platforms and focus of the researches. It will show the trend in the research subjects by countries, in addition to the activity trends, platforms usage trend and others. Further analysis of the most employed approach (Lexicons) and data (text) will be also shown. Finally, challenges and future works are drawn and proposed.

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Section: S6-rq1: What Are the Datasets Used By Researchers?mentioning

confidence: 99%

Section: S6-rq1: What Are the Datasets Used By Researchers?mentioning

confidence: 99%

Investigating transportation research based on social media analysis: a systematic mapping review

et al. 2021

View full text Add to dashboard Cite

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“…Sentimental analysis involves various research fields such as product recommendation [22], flight service [17] and opinion mining [23]. The data used in sentiment analysis are collected from online networks such as micro-blogs [24] and health forums [25].…”

Section: A Sentimental Analysis In Social Mediamentioning

confidence: 99%

“…However, the extant experimental results show that word-level emotional scores are insufficient for capturing richer emotional expression [16]. Moreover, researchers [14], [17] have suggested that sentimental analysis is effective in extracting ADRs from social texts. In fact, some emotions are often implied in the whole semantic representation of posts.…”

Section: Introductionmentioning

confidence: 99%

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

Lin

Zheng

2020

IEEE Access

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Discovering more time-effective and a wider range of adverse drug reactions (ADRs) from social texts related to feelings concerning taking medication has recently received significant interest in pharmacovigilance research. Recognizing the posts that include ADRs is an important step for detecting ADRs from social texts. The existing systems show the unsatisfactory performance due to the insufficient expression of emotions and the inadequacy of information expression in short social texts. Although these systems exploit emotional features to improve the performance of their methods, the representation of wordlevel emotional scores is insufficient for emotional expression. Moreover, most of the systems make less use of medical knowledge to enhance the detection of the potential relationship between drugs and adverse reactions in posts. Therefore, enough expression of emotion and medical knowledge in sparse medical social texts may be explored to improve system performance. This paper proposed an effective method integrating sufficient emotional expression and medical knowledge to detect ADRs from medical tweets. First, the proposed method utilized sentence-level emotional context and word-level emotional score to learn sufficient emotional information for distinguishing between ADR and non-ADR tweets. Furthermore, a co-occurrence dictionary of each drug and its relevant ADRs was constructed by means of a medical resource (MedDRA) and drug site (www.drug.com) to help the proposed model focus on posts containing drugs and ADRs. Finally, a convolutional neural network (CNN) model on the basis of bidirectional encoder representations from transformers (BERT) performed the classification task. The proposed model achieved better overall performance than the other existing methods on two Twitter datasets (F1-scores of 72.64% and 64.98% on PSB2016 and SMM4H, respectively). INDEX TERMS Adverse drug reaction, medical knowledge, emotional context, co-occurrence dictionary, social text.

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“…Dutta et al [32] analyzed airline twitter data using Naïve Bayes classifier for sentiment analysis. They used R and Rapid Miner tools to develop the classification model and map the tweets into positive, negative and neutral category.…”

Section: Related Workmentioning

confidence: 99%

A machine learning approach to analyze customer satisfaction from airline tweets

Kumar

Zymbler

2019

J Big Data

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IntroductionAdvancement in technology has boosted the availability and use of smart mobile phones. At present, the number of smart phone users is 2.71 billion across the world [1]. The major online social media (SM) platforms i.e. Facebook, Twitter and Instagram are available as mobile applications in the smart phones. Therefore, there is no need to visit cyber cafes to access them, as everything is available in the smart phones.Every piece of information shared on SM carries an emotion, sentiment or feeling. These emotions can be positive, negative and neutral. All these emotions may come from a travel trip, restaurant trip, exhibitions, movies, elections, hospital visits etc. These emotions carries some hidden information related to comfort/discomfort in related areas. Hence, there is a good scope of analyzing this information to detect the patterns of the emotions. This analysis can help us to understand the emotions of the people in respective domain and the reasons behind it.Air travel is one of the most convenient mode for long distance travel at both national and international level [2]. There are many airline service providers (ASPs) around the world. The competitive world motivates the airlines company to attract the customers. AbstractCustomer'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. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Sentimental Analysis for Airline Twitter data

Cited by 26 publications

References 2 publications

Investigating transportation research based on social media analysis: a systematic mapping review

Investigating transportation research based on social media analysis: a systematic mapping review

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

A machine learning approach to analyze customer satisfaction from airline tweets

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