Sentimental Analysis for Airline Twitter data

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.

Abstract: People nowadays use emoticons in their text in increasing order in order to express their feelings or recapitulate their words. Earlier machine learning techniques only involve the classification of text, emoticons or images solely where emoticons with text have always been neglected, thus ignored lots of emotions. This research proposed an algorithm and method for sentiment analysis using both text and emoticon. In this work, both modes of data were analyzed in combined and separately with both machine learning and deep learning algorithms for finding sentiments from twitter based airline data using several features such as TF-IDF, Bag of words, N-gram, and emoticon lexicons. This research demonstrates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual data analysis. Also, deep learning algorithms are found to be better than machine learning algorithms.

Abstract: Game Development is one of the most important emerging fields in software engineering era. Game addiction is the nowadays disease which is combined with playing computer and videogames. Shame is a negative feeling about self evaluationas well as guilt that is considered as a negative evaluation of the transgressing behaviour, both are associated withadaptive and concealing responses. Sentiment analysis demonstrates a huge progression towards the understanding of web users’ opinions. In this paper, the sentiments of game developers are examined to measure their guilt’s emotions when working in this career. The sentiment analysis model is implementedthrough the following steps: sentiment collector, sentiment pre-processing, and then machine learning methods were used. The model classifies sentiments into guilt or no guilt and is trained with 1000 Reddit website sentiment. Results have shown that Support Vector Machine (SVM) approach is more accurate incomparison to Naive Bayes (NV) and Decision Tree.

Abstract: Nowadays, a million users use social networking services such as Twitter to tweet their products and services by placing the reviews based on their opinions. Sentiment analysis has emerged to analyze the twitter data automatically. Sentiment classification techniques used to classify US airline tweets based on sentiment polarity due to flight services as positive, negative and neutral connotations done on six different US airlines. To detect sentiment polarity, we explored word embedding models (Word2Vec, Glove) in tweets using deep learning methods. Here, we investigated sentiment analysis using the Recurrent Neural Network (RNN) model along with Long-Short Term Memory networks (LSTMs) units can deal with long term dependencies by introducing memory in a network model for prediction and visualization. The results showed better significant classification accuracy trained 80% for training set and 20% for testing set which shows that our models are reliable for future prediction. To improve this performance, the Bidirectional LSTM Model (Bi-LSTM) is used for further investigation studies.

Abstract: The text mining technology enables researchers or enterprises to automatically and efficiently access the information in text comments. This paper obtains 24,165 reviews from SKYTRAX between September 2011 and March 2019, 5700 of which express that passengers had experienced flight delays. It uses sentiment analysis based on a sentiment dictionary to classify user reviews and uses co-occurrence analysis to identify passengers' concerns on different aspects of service in the aviation industry. The results of the user sentiment analysis show that there is a significant and negative correlation between the user's emotions and their flight delay experiences. After flight delay, passengers' attention to the service aspects has increased, while satisfaction with the airport service has dropped dramatically. This paper shed some new light on public opinion about flight delays.

Abstract: From the publisher: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

Abstract: Statistical approaches to processing natural language text have become dominant in recent years This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear The book contains all the theory and algorithms needed for building NLP tools It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications

Abstract: Introduction to support vector learning roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik generalization performance of support vector machines and other pattern classifiers, Peter Bartlett and John Shawe-Taylor Bayesian voting schemes and large margin classifiers, Nello Cristianini and John Shawe-Taylor support vector machines, reproducing kernel Hilbert spaces, and randomized GACV, Grace Wahba geometry and invariance in kernel based methods, Christopher J.C. Burges on the annealed VC entropy for margin classifiers - a statistical mechanics study, Manfred Opper entropy numbers, operators and support vector kernels, Robert C. Williamson et al. Part 2 Implementations: solving the quadratic programming problem arising in support vector classification, Linda Kaufman making large-scale support vector machine learning practical, Thorsten Joachims fast training of support vector machines using sequential minimal optimization, John C. Platt. Part 3 Applications: support vector machines for dynamic reconstruction of a chaotic system, Davide Mattera and Simon Haykin using support vector machines for time series prediction, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel. Part 4 Extensions of the algorithm: reducing the run-time complexity in support vector machines, Edgar E. Osuna and Federico Girosi support vector regression with ANOVA decomposition kernels, Mark O. Stitson et al support vector density estimation, Jason Weston et al combining support vector and mathematical programming methods for classification, Bernhard Scholkopf et al.

Abstract: In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervied approach to the problem, but leverage existing hashtags in the Twitter data for building training data.

Abstract: Target-dependent sentiment analysis on Twitter has attracted increasing research attention. Most previous work relies on syntax, such as automatic parse trees, which are subject to noise for informal text such as tweets. In this paper, we show that competitive results can be achieved without the use of syntax, by extracting a rich set of automatic features. In particular, we split a tweet into a left context and a right context according to a given target, using distributed word representations and neural pooling functions to extract features. Both sentiment-driven and standard embeddings are used, and a rich set of neural pooling functions are explored. Sentiment lexicons are used as an additional source of information for feature extraction. In standard evaluation, the conceptually simple method gives a 4.8% absolute improvement over the state-of-the-art on three-way targeted sentiment classification, achieving the best reported results for this task.