Predicting sentiment toward transportation in social media using visual and textual features

Giancristofaro, Gabriel T.; Panangadan, Anand

doi:10.1109/itsc.2016.7795898

Cited by 16 publications

(6 citation statements)

References 13 publications

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“…The ensemble classifier was Random Forest on a publicly available MVSA dataset [51] in English. Another approach is sentiment analysis on the transportation domain described in article [52]. The study in the article uses Instagram's captions, images, and comments.…”

Section: Fusion Of Image-text For Sentiment Analysismentioning

confidence: 99%

Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts

Setiawan¹

2021

IJIES

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Social media development makes it possible for everyone to express their opinions and information through text, speech, video, or images. Multiview sentiment analysis in current studies generally combines two modalities, text and image. It seeks to classify social media posts into two or more polarities, such as positive, neutral, or negative. To improve the performance of multiview Sentiment Analysis, we added another modality, which is concepts derived from text and image. Our proposed model integrates three views into a fusion with an ensemble approach by a metaclassifier. We performed text classification with Deep Convolutional Neural Networks. The input feature is Word2Vec for text representation in order to preserve semantic meaning. Additionally, we analyzed concepts from texts with SenticNet 5 as a knowledge base model and extracted concepts from images using the DeepSentiBank model. We obtained 2089 Adjective Noun Pairs and classified it with Multi-Layer Perceptron. Then we combined predicted probabilities from each classifier for Image, Text, and Concept by Ensemble Learning. A meta-classifier was implemented to predict the final sentiment from a fusion of Image-Text-Concept features. The fusion for multiview sentiment analysis works well and could achieve the best accuracy of 70% by applying the ensemble approach with Logistic Regression as the meta-classifier.

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Section: Fusion Of Image-text For Sentiment Analysismentioning

confidence: 99%

Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts

Setiawan¹

2021

IJIES

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“…Zhang et al, 2015). Multiple researches compared the two, and some of these researches compared them to other approaches including decision trees (DT) and for the same previous aims; (Alamsyahl et al, 2018;Anastasia & Budi, 2016;Giancristofaro et al, 2016;Gupta et al, 2018;Rane & Kumar, 2018;Z. 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.…”

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

confidence: 99%

“…BOW converts text data into a numerical form that ML algorithms and others beside computers can understand. (Chen et al, 2018;Gal-Tzur et al, 2014;Giancristofaro et al, 2016;Liau & Tan, 2014;Musaev et al, 2018;Pournarakis et al, 2017;Rane & Kumar, 2018). N-gram is a model under computational linguistics and refers to sequence extraction from text or speech, so generating n-grams is included in the preprocessing stage of texts in the studies (Ali et al, 2018;Daly et al, 2013;Windasari et al, 2017).…”

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

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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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“…[15] [16] Presented an approach that integrates the text mining techniques and ensemble methods which increases the performance of models for predicting the severity of rail accidents. [17] Explores the prediction of transportation sentiment classification using Instagram social media features. The combination of visual and textual features increases the accuracy of predicting transportation sentiment classification.…”

Section: Related Researchmentioning

confidence: 99%

Opinion Mining on Social Media Transit Tweets using Text Pre-Processing and Machine Learning Techniques

Rani¹,

Sumathy²

2019

IJITEE

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Capturing public insights related to transit systems in social media has gained huge popularity presently. The regional transportation agencies use social media as a tool to provide information to the public and seek their inputs and ideas for meaningful decision making in transportation activities. This exploratory study attempts to gauge the impact of social media use in transportation planning that in turn would help transportation administration in identifying the day-to-day challenges faced by the customers and to suggest a suitable solution. This paper presents the effect of pre-processing techniques on transit opinion analysis to improve the performance. Performance of different pre-processing methods namely stop word removal, stemming, lemmatization, negation handling and URL removal using feature representation models namely TF-IDF with unigram, TF-IDF with bigram on three feature selection techniques including information gain, standard deviation and chi-square on social media transit rider’s opinion is carried out. The experimental results are evaluated using four different classifiers such as Support vector machine, Naïve Bayes, Decision Tree, K-Nearest Neighborhood in terms of accuracy, precision, recall, and f-measure. On analyzing the social media related transit opinion data, it is observed that pre-processing with bigram technique performs better than the other approaches specifically with Support Vector Machine and Naïve Bayes.

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Predicting sentiment toward transportation in social media using visual and textual features

Cited by 16 publications

References 13 publications

Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts

Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts

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

Opinion Mining on Social Media Transit Tweets using Text Pre-Processing and Machine Learning Techniques

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