Data Mining Practical Machine Learning Tools and Techniques

This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.

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SemEval-2017 Task 4: Sentiment Analysis in Twitter

This paper discusses the fourth year of the ”Sentiment Analysis in Twitter Task”. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new subtasks focus on two variants of the basic “sentiment classification in Twitter” task. The first variant adopts a five-point scale, which confers an ordinal character to the classification task. The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature. The task continues to be very popular, attracting a total of 43 teams.

SemEval-2016 Task 4: Sentiment Analysis in Twitter

Considerable progress has been made in recent years in the development of dialogue systems that support robust and efficient human–machine interaction using spoken language. Spoken dialogue technology allows various interactive applications to be built and used for practical purposes, and research focuses on issues that aim to increase the system’s communicative competence by including aspects of error correction, cooperation, multimodality, and adaptation in context. This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems. It provides an overview of the basic issues such as system architectures, various dialogue management methods, system evaluation, and also surveys advanced topics concerning extensions of the basic model to more conversational setups. The goal of the book is to provide an introduction to the methods, problems, and solutions that are used in dialogue system development and evaluation. It presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research. vi KEywoRDS Spoken dialogue systems, multimodality, evaluation, error-handling, dialogue management, statistical method v MC_Jok nen_FM. ndd Achorn Internat onal 10/10/2009 04:18AM

https://www.morganclaypool.com/doi/suppl/10.2200/S00509ED1V01Y201305HLT023/suppl_file/Dagan_Ch1.pdf

Synthesis Lectures on Human Language Technologies

This paper introduces ASTD, an Arabic social sentiment analysis dataset gathered from Twitter. It consists of about 10,000 tweets which are classified as objective, subjective positive, subjective negative, and subjective mixed. We present the properties and the statistics of the dataset, and run experiments using standard partitioning of the dataset. Our experiments provide benchmark results for 4 way sentiment classification on the dataset.

/pdf/astd-arabic-sentiment-tweets-dataset-4ijmj98pwz.pdf

ASTD: Arabic Sentiment Tweets Dataset

We present a newly collected data set of 8,868 gold-standard annotated Arabic feeds. The corpus is manually labelled for subjectivity and sentiment analysis (SSA) ( = 0:816). In addition, the corpus is annotated with a variety of motivated feature-sets that have previously shown positive impact on performance. The paper highlights issues posed by twitter as a genre, such as mixture of language varieties and topic-shifts. Our next step is to extend the current corpus, using online semi-supervised learning. A first sub-corpus will be released via the ELRA repository as part of this submission.

/pdf/an-arabic-twitter-corpus-for-subjectivity-and-sentiment-1af71mf01n.pdf

An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis

We present a study on sentence-level Arabic Dialect Identification using the newly developed Multidialectal Parallel Corpus of Arabic (MPCA) – the first experiments on such data. Using a set of surface features based on characters and words, we conduct three experiments with a linear Support Vector Machine classifier and a meta-classifier using stacked generalization – a method not previously applied for this task. We first conduct a 6-way multi-dialect classification task in the first experiment, achieving \(74\,\%\) accuracy against a random baseline of \(16.7\,\%\) and demonstrating that meta-classifiers can large performance increases over single classifiers. The second experiment investigates pairwise binary dialect classification within the corpus, yielding results as high as \(94\,\%\), but also highlighting poorer results between closely related dialects such as Palestinian and Jordanian (\(76\,\%\)). Our final experiment conducts cross-corpus evaluation on the widely used Arabic Online Commentary (AOC) dataset and demonstrates that despite differing greatly in size and content, models trained with the MPCA generalize to the AOC, and vice versa. Using only 2, 000 sentences from the MPCA, we classify over 26 k sentences from the radically different AOC dataset with \(74\,\%\) accuracy. We also use this data to classify a new dataset of MSA and Egyptian Arabic tweets with \(97\,\%\) accuracy. We find that character n-g are a very informative feature for this task, in both within- and cross-corpus settings. Contrary to previous results, they outperform word n-grams in several experiments here. Several directions for future work are outlined.

/pdf/arabic-dialect-identification-using-a-parallel-4szij2o8k2.pdf

Arabic Dialect Identification Using a Parallel Multidialectal Corpus

This paper describes the iLab-Edinburgh Sentiment Analysis system, winner of the Arabic Twitter Task 7 in SemEval-2016. The system employs a hybrid approach of supervised learning and rule-based methods to predict a sentiment intensity (SI) score for a given Arabic Twitter phrase. First, the supervised method uses an ensemble of trained linear regression models to produce an initial SI score for each given text instance. Second, the resulting SI score is adjusted using a set of rules that exploit a number of publicly available sentiment lexica. The system demonstrates strong results of 0.536 Kendall score, ranking top in this task.

A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases

Traditional approaches to Sentiment Analysis (SA) rely on large annotated data sets or wide-coverage sentiment lexica, and as such often perform poorly on under-resourced languages. This paper presents empirical evidence of an efficient SA approach using freely available machine translation (MT) systems to translate Arabic tweets to English, which we then label for sentiment using a state-of-theart English SA system. We show that this approach significantly outperforms a number of standard approaches on a gold-standard heldout data set, and performs equally well compared to more cost-intense methods with 76% accuracy. This confirms MT-based SA as a cheap and effective alternative to building a fully fledged SA system when dealing with under-resourced languages.

/pdf/benchmarking-machine-translated-sentiment-analysis-for-2kd0r4ds4m.pdf

Benchmarking Machine Translated Sentiment Analysis for Arabic Tweets

The Internet of Things (IoT) has impacted various aspects of life, but its profound effects on the health sector are particularly striking because of its cutting-edge nature. Mobile computing characteristics enable IoT to play a more important role when used with mobile computing. A significant part of the benefits of IoT in healthcare can be attributed to mobile health, which is greatly enhanced by mobile computing. Wearables transmit large amounts of data to IoT devices through sensors, actuators, and transceivers. Threats, attacks, and vulnerabilities abound for data on the Internet of Things. Therefore, addressing IoT-related security, privacy, and vulnerability issues call for a robust security solution. This paper proposes a secure and scalable healthcare data transmission framework in IoT based on an optimized routing protocol. Initially, the health data is collected from various IoT devices like wearable devices and sensors. The raw data is preprocessed via data cleaning and data reduction techniques. K-nearest neighbor (KNN) imputation is performed and principal component analysis (PCA) is employed for dimension reduction of the data. Utilizing modified local binary patterns (MLBP), the features are extracted from the preprocessed data. By combining the fuzzy dynamic trust-based RPL algorithm with the butter ant optimization (BAO) algorithm for low-power and lossy networks, the proposed fuzzy dynamic trust-based RPL (FDT-RPL) protocol improves the overall security of data transmission. The algorithm has been implemented for a smart healthcare system, and the performance is analyzed by comparing it with traditional approaches. The proposed routing protocol provided a secure and scalable healthcare data transmission.

/pdf/secure-and-scalable-healthcare-data-transmission-in-iot-4trbsgq5.pdf

Eshrag Refaee

Papers

An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis

Arabic Dialect Identification Using a Parallel Multidialectal Corpus

A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases

Benchmarking Machine Translated Sentiment Analysis for Arabic Tweets

Secure and Scalable Healthcare Data Transmission in IoT Based on Optimized Routing Protocols for Mobile Computing Applications