LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification

Vilares, David; Doval, Yerai; Alonso, Miguel Ángel Verdugo; Gómez-Rodríguez, Carlos

doi:10.18653/v1/s16-1009

Cited by 9 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They extracted features based on the lexical content of each tweet and adopted sentiment-specific lexicons. Vilares et al (2016) trained a convolutional neural network using pre-trained Twitter word embeddings to implement multi-class TSA. Likewise, Ruder et al (2016) also applied deep learning-based model for multi-class TSA.…”

Section: Related Workmentioning

confidence: 99%

Multi-class Twitter sentiment classification with emojis

Ch’ng

Chong

et al. 2018

IMDS

View full text Add to dashboard Cite

Purpose Recently, various Twitter Sentiment Analysis (TSA) techniques have been developed, but little has paid attention to the microblogging feature – emojis, and few works have been conducted on the multi-class sentiment analysis of tweets. The purpose of this paper is to consider the popularity of emojis on Twitter and investigate the feasibility of an emoji training heuristic for multi-class sentiment classification of tweets. Tweets from the “2016 Orlando nightclub shooting” were used as a source of study. Besides, this study also aims to demonstrate how mapping can contribute to interpreting sentiments. Design/methodology/approach The authors presented a methodological framework to collect, pre-process, analyse and map public Twitter postings related to the shooting. The authors designed and implemented an emoji training heuristic, which automatically prepares the training data set, a feature needed in Big Data research. The authors improved upon the previous framework by advancing the pre-processing techniques, enhancing feature engineering and optimising the classification models. The authors constructed the sentiment model with a logistic regression classifier and selected features. Finally, the authors presented how to visualise citizen sentiments on maps dynamically using Mapbox. Findings The sentiment model constructed with the automatically annotated training sets using an emoji approach and selected features performs well in classifying tweets into five different sentiment classes, with a macro-averaged F-measure of 0.635, a macro-averaged accuracy of 0.689 and the MAEM of 0.530. Compared to those experimental results in related works, the results are satisfactory, indicating the model is effective and the proposed emoji training heuristic is useful and feasible in multi-class TSA. The maps authors created, provide a much easier-to-understand visual representation of the data, and make it more efficient to monitor citizen sentiments and distributions. Originality/value This work appears to be the first to conduct multi-class sentiment classification on Twitter with automatic annotation of training sets using emojis. Little attention has been paid to applying TSA to monitor the public’s attitudes towards terror attacks and country’s gun policies, the authors consider this work to be a pioneering work. Besides, the authors have introduced a new data set of 2016 Orlando Shooting tweets, which will be made available for other researchers to mine the public’s political opinions about gun policies.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-class Twitter sentiment classification with emojis

Ch’ng

Chong

et al. 2018

IMDS

View full text Add to dashboard Cite

show abstract

“…In terms of algorithms, various machine-learning models, such as Naïve Bayes [15,16], Ensemble [17], or Deep Learning Structure [18] have been used for crime prediction, but Deep Neural Networks (DNN) provided better results in our previous experiments. This study uses DNN because it reflects representation learning and has been used in crosslingual transfer [19], speech recognition [20][21][22][23], image recognition [24][25][26][27], sentiment analysis [28][29][30][31][32], and biomedical [33]. Although the upper bound of the prediction performance still depends on the problem and the data themselves, DNN's auto-feature extraction [34] allows us to use rapid model building without feature processing, thus reducing the application threshold due to feature processing.…”

Section: Related Workmentioning

confidence: 99%

Grid-Based Crime Prediction Using Geographical Features

Lin

Yen

2018

IJGI

View full text Add to dashboard Cite

Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design's baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.

show abstract

“…Our experiments suggest that our approach better deals with these phenomena than lexical-based systems. We also have developed machine learning models that have been evaluated in international evaluation campaigns [5,6].…”

Section: Sentiment Analysismentioning

confidence: 99%