Sentiment Analysis for micro-blogging platforms in Arabic

Sentiment Analysis (SA) concerns the automatic extraction and classification of sentiments conveyed in a given text, i.e. labelling a text instance as positive, negative or neutral. SA research has attracted increasing interest in the past few years due to its numerous real-world applications. The recent interest in SA is also fuelled by the growing popularity of social media platforms (e.g. Twitter), as they provide large amounts of freely available and highly subjective content that can be readily crawled. Most previous SA work has focused on English with considerable success. In this work, we focus on studying SA in Arabic, as a less-resourced language. This work reports on a wide set of investigations for SA in Arabic tweets, systematically comparing three existing approaches that have been shown successful in English. Specifically, we report experiments evaluating fully-supervised-based (SL), distantsupervision- based (DS), and machine-translation-based (MT) approaches for SA. The investigations cover training SA models on manually-labelled (i.e. in SL methods) and automatically-labelled (i.e. in DS methods) data-sets. In addition, we explored an MT-based approach that utilises existing off-the-shelf SA systems for English with no need for training data, assessing the impact of translation errors on the performance of SA models, which has not been previously addressed for Arabic tweets. Unlike previous work, we benchmark the trained models against an independent test-set of >3.5k instances collected at different points in time to account for topic-shifts issues in the Twitter stream. Despite the challenging noisy medium of Twitter and the mixture use of Dialectal and Standard forms of Arabic, we show that our SA systems are able to attain performance scores on Arabic tweets that are comparable to the state-of-the-art SA systems for English tweets. The thesis also investigates the role of a wide set of features, including syntactic, semantic, morphological, language-style and Twitter-specific features. We introduce a set of affective-cues/social-signals features that capture information about the presence of contextual cues (e.g. prayers, laughter, etc.) to correlate them with the sentiment conveyed in an instance. Our investigations reveal a generally positive impact for utilising these features for SA in Arabic. Specifically, we show that a rich set of morphological features, which has not been previously used, extracted using a publicly-available morphological analyser for Arabic can significantly improve the performance of SA classifiers. We also demonstrate the usefulness of languageindependent features (e.g. Twitter-specific) for SA. Our feature-sets outperform results reported in previous work on a previously built data-set

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field.Fil: Tessore, Juan Pablo. Universidad Nacional del Noroeste de la Pcia.de Bs.as.. Escuela de Tecnologia. Instituto de Investigacion y Transferencia En Tecnologia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Instituto de Investigacion y Transferencia En Tecnologia.; ArgentinaFil: Esnaola, Leonardo Martín. Universidad Nacional del Noroeste de la Pcia.de Bs.as.. Escuela de Tecnologia. Instituto de Investigacion y Transferencia En Tecnologia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Instituto de Investigacion y Transferencia En Tecnologia.; ArgentinaFil: Lanzarini, Laura Cristina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; ArgentinaFil: Baldassarri, Sandra Silvia. Universidad de Zaragoza; Españ

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field

Comparative Evaluation of Sentiment Analysis Methods Across Arabic Dialects

Sentiment analysis in Arabic is challenging due to the complex morphology of the language. The task becomes more challenging when considering Twitter data that contain significant amounts of noise such as the use of Arabizi, code-switching and different dialects that varies significantly across the Arab world, the use of non-Textual objects to express sentiments, and the frequent occurrence of misspellings and grammatical mistakes. Modeling sentiment in Twitter should become easier when we understand the characteristics of Twitter data and how its usage varies from one Arab region to another. We describe our effort to create the first Multi-Dialect Arabic Sentiment Twitter Dataset (MD-ArSenTD) that is composed of tweets collected from 12 Arab countries, annotated for sentiment and dialect. We use this dataset to analyze tweets collected from Egypt and the United Arab Emirates (UAE), with the aim of discovering distinctive features that may facilitate sentiment analysis. We also perform a comparative evaluation of different sentiment models on Egyptian and UAE tweets. These models are based on feature engineering and deep learning, and have already achieved state-of-The-Art accuracies in English sentiment analysis. Results indicate the superior performance of deep learning models, the importance of morphological features in Arabic NLP, and that handling dialectal Arabic leads to different outcomes depending on the country from which the tweets are collected.This work was made possible by NPRP 6-716-1-138 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

MS-TR: A Morphologically Enriched Sentiment Treebank and Recursive Deep Models for Compositional Semantics in Turkish

Recursive Deep Models have been used as powerful models to learn compositional representations of text for many natural language processing tasks. However, they require structured input (i.e. sentiment treebank) to encode sentences based on their tree-based structure to enable them to learn latent semantics of words using recursive composition functions. In this paper, we present our contributions and efforts for the Turkish Sentiment Treebank construction. We introduce MS-TR, a Morphologically Enriched Sentiment Treebank, which was implemented for training Recursive Deep Models to address compositional sentiment analysis for Turkish, which is one of the well-known Morphologically Rich Language (MRL). We propose a semi-supervised automatic annotation, as a distantsupervision approach, using morphological features of words to infer the polarity of the inner nodes of MS-TR as positive and negative. The proposed annotation model has four different annotation levels: morph-level, stem-level, token-level, and review-level. Each annotation level’s contribution was tested using three different domain datasets, including product reviews, movie reviews, and the Turkish Natural Corpus essays. Comparative results were obtained with the Recursive Neural Tensor Networks (RNTN) model which is operated over MS-TR, and conventional machine learning methods. Experiments proved that RNTN outperformed the baseline methods and achieved much better accuracy results compared to the baseline methods, which cannot accurately capture the aggregated sentiment information

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field.Instituto de Investigación en Informátic

COVID-19 misinformation on Twitter: the role of deceptive support

2022 Summer.Includes bibliographical references.Social media platforms like Twitter are a major dissemination point for information and the COVID-19 pandemic is no exception. But not all of the information comes from reliable sources, which raises doubts about their validity. In social media posts, writers reference news articles to gain credibility by leveraging the trust readers have in reputable news outlets. However, there is not always a positive correlation between the cited article and the social media posting. Targeting the Twitter platform, this study presents a novel pipeline to determine whether a Tweet is indeed supported by the news article it refers to. The approach follows two general objectives: to develop a model capable of detecting Tweets containing claims that are worthy of fact-checking and then, to assess whether the claims made in a given Tweet are supported by the news article it cites. In the event that a Tweet is found to be trustworthy, we extract its claim via a sequence labeling approach. In doing so, we seek to reduce the noise and highlight the informative parts of a Tweet. Instead of detecting erroneous and invalid information by analyzing the propagation patterns or ensuing examination of Tweets against already proven statements, this study aims to identify reliable support (or lack thereof) before misinformation spreads. Our research reveals that 14.5% of the Tweets are not factual and therefore not worth checking. An effective filter like this is especially useful when looking at a platform such as Twitter, where hundreds of thousands of posts are created every day. Further, our analysis indicates that among the Tweets which refer to a news article as evidence of a factual claim, at least 1% of those Tweets are not substantiated by the article, and therefore mislead the reader