Sentimental Analysis for Airline Twitter data
Deb Dutta Das,Sharan Sharma,Shubham Natani,Neelu Khare,Brijendra Singh +4 more
- Vol. 263, Iss: 4, pp 042067
TLDR
This paper is classifying sentiment of Twitter messages by exhibiting results of a machine learning algorithm using R and Rapid Miner and categorizing them in neutral, negative and positive sentiments finally summarising the results as a whole.Abstract:
Social Media has taken the world by surprise at a swift and commendable pace. With the advent of any kind of circumstances may it be related to social, political or current affairs the sentiments of people throughout the world are expressed through their help, making them suitable candidates for sentiment mining. Sentimental analysis becomes highly resourceful for any organization who wants to analyse and enhance their products and services. In the airline industries it is much easier to get feedback from astute data source such as Twitter, for conducting a sentiment analysis on their respective customers. The beneficial factors relating to twitter sentiment analysis cannot be impeded by the consumers who want to know the who's who and what's what in everyday life. In this paper we are classifying sentiment of Twitter messages by exhibiting results of a machine learning algorithm using R and Rapid Miner. The tweets are extracted and pre-processed and then categorizing them in neutral, negative and positive sentiments finally summarising the results as a whole. The Naive Bayes algorithm has been used for classifying the sentiments of recent tweets done on the different airlines.read more
Citations
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A machine learning approach to analyze customer satisfaction from airline tweets
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An algorithm and method for sentiment analysis using the text and emoticon
TL;DR: It is demonstrated that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual data analysis, and deep learning algorithms are found to be better than machine learning algorithms.
Proceedings ArticleDOI
Sentiment Analysis of US Airlines Tweets Using LSTM/RNN
TL;DR: This work investigated sentiment analysis using the Recurrent Neural Network (RNN) model along with Long-Short Term Memory networks (LSTMs) units to deal with long term dependencies by introducing memory in a network model for prediction and visualization.
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Analyzing passengers’ emotions following flight delays- a 2011–2019 case study on SKYTRAX comments
Cen Song,Jingquan Guo,Jun Zhuang +2 more
TL;DR: There is a significant and negative correlation between the user's emotions and their flight delay experiences, and some new light on public opinion about flight delays is shed.
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Consumer recommendation prediction in online reviews using Cuckoo optimized machine learning models
TL;DR: In this paper, a cuckoo optimized machine learning model was proposed to predict airline recommendations using data from Skytrax, and the proposed eXtreme gradient boosting classifier optimized by Cuckoo Search (CS-XGB) outperformed other stateof-the-art techniques.
References
More filters
Proceedings Article
On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter
TL;DR: The results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches and the dynamic generation of stopword lists appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and shrinking the feature space.
Proceedings ArticleDOI
Tweet Sentiment: From Classification to Quantification
Wei Gao,Fabrizio Sebastiani +1 more
TL;DR: It is shown, on a multiplicity of TSC datasets, that using a quantification-specific algorithm produces substantially better class frequency estimates than a state-of-the-art classification-oriented algorithm routinely used in TSC.
Proceedings ArticleDOI
Wikipedia based semantic smoothing for twitter sentiment classification
TL;DR: Wikipedia based semantic smoothing approach is proposed by using Wikipedia article titles that exist in training documents, categories and redirects of these articles as topic signatures and shows that it improves the performance of NB and even can exceed the accuracy of SVM on Twitter Sentiment 140 dataset.
Proceedings ArticleDOI
Message from SNAA 2014 program chairs
TL;DR: Social networks have been investigated for many years but until recently the scope of the analyses was limited due to the small size of the available data samples, usually collected through questionnaires and interviews.
Journal ArticleDOI
Fuzzy soft set decision-making model for social networking sites
TL;DR: F fuzzy soft set decision-making model is presented, which gives a new hypothesis for determining the popular social networking sites by involving significant parameters, and the experimental result shows that the FSS decision- making model provides a new algorithm which is to determine the most popular networking site.
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