Sentiment lexicon for sentiment analysis of Saudi dialect tweets

Al-Thubaity, Abdulmohsen; Alqahtani, Qubayl; Aljandal, Abdulaziz

doi:10.1016/j.procs.2018.10.494

Cited by 44 publications

(22 citation statements)

References 27 publications

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“…They assumed that K-NN is the best classifier in their situation, because it gives more accurate predictions than Naïve-Bayes. Furthermore, the authors in [ 4 ] worked on sentiment lexicon for sentiment analysis of Saudi dialect tweets (SaudiSenti) and compared it to Large Arabic sentiment dictionary (AraSenti). For that, they used a dataset of tweets previously labeled, that comprises 5400 tweets dealing with various topics.…”

Section: Literature Surveymentioning

confidence: 99%

Topic Modeling and Sentiment Analysis with LDA and NMF on Moroccan Tweets

Habbat¹,

Anoun²,

Hassouni³

2021

Innovations in Smart Cities Applications Volume 4

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Twitter is one of the most popular social media platforms. Due to its simplicity of use and the services provided by twitter API, it is extensively used around the world, including Morocco. It provides huge volume of information and is considered as a large source of data for opinion mining. The aim of this paper is to analyze Moroccan tweets, in order to generate some useful statistics, identify different sentiments, and extract then visualize predominant topics. In our research work, we collected 25 146 tweets using Twitter API and python language, and stored them into MongoDB database. Stored tweets were preprocessed by applying natural language processing techniques (NLP) using NLTK library. Then, we performed sentiment analysis which classifies the polarity of twitter comments into negative, positive, and neutral categories. Finally, we applied topic modeling over the tweets to obtain meaningful data from Twitter, comparing and analyzing topics detected by two popular topic modeling algorithms; Non-negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA). The observed results show that LDA outperforms NMF in terms of their topic coherence.

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Section: Literature Surveymentioning

confidence: 99%

Topic Modeling and Sentiment Analysis with LDA and NMF on Moroccan Tweets

Habbat¹,

Anoun²,

Hassouni³

2021

Innovations in Smart Cities Applications Volume 4

View full text Add to dashboard Cite

show abstract

“…The lexicon was built using two methods. The first method was by using previously constructed lexicons for Arabic sentiment (Al-Thubaity et al, 2018;Al-Twairesh et al, 2016;Salameh et al, 2015). These lexicons either did not contain words specifically suited for Saudi dialect or they where domain specific.…”

Section: Sentiment Lexiconmentioning

confidence: 99%

Tracking And Understanding Public Reaction During COVID-19: Saudi Arabia As A Use Case

Addawood¹,

Alsuwailem²,

Alohali³

et al. 2020

Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

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The coronavirus disease of 2019 (COVID-19) has a huge impact on economies and societies around the world. While governments are taking extreme measures to reduce the spread of the virus, people are getting affected by these new measures. With restrictions like lockdown and social distancing, it became important to understand the emotional response of the public towards the pandemic. In this paper, we study the reaction of Saudi Arabia citizens towards the pandemic. We utilize a collection of Arabic tweets that were sent during 2020, primarily through hashtags that were originated from Saudi Arabia. Our results showed that people had kept a positive reaction towards the pandemic. This positive reaction was at its highest at the beginning of the COVID-19 crisis and started to decline as time passes. Overall, the results showed that people were so supportive of each other through this pandemic. This research can help researchers and policymakers in understanding the emotional effect of a pandemic on societies.

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“…The lexicon consists of Twitter data, which is drawn from various Saudi and other Arabic dialects. In another study [23], the authors presented a sentiment lexicon consisting of around 6000 tweets in modern standard Arabic. They used trending hash tags in Saudi Arabia to collect their data.…”

Section: Related Workmentioning

confidence: 99%

A Corpus Based Approach to Build Arabic Sentiment Lexicon

Alsolamy¹,

Siddiqui²,

Khan³

2019

IJIEEB

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Sentiment analysis is an application of artificial intelligence that determines the sentiment associated sentiment with a piece of text. It provides an easy alternative to a brand or company to receive customers' opinions about its products through user generated contents such as social media posts. Training a machine learning model for sentiment analysis requires the availability of resources such as labeled corpora and sentiment lexicons. While such resources are easily available for English, it is hard to find them for other languages such as Arabic. The aim of this research is to build an Arabic sentiment lexicon using a corpus-based approach. Sentiment scores were propagated from a small, manually labeled, seed list to other terms in a term co-occurrence graph. To achieve this, we proposed a graph propagation algorithm and compared different similarity measures. The lexicon was evaluated using a manually annotated list of terms. The use of similarity measures depends on the fact that the words that are appearing in the same context will have similar polarity. The main contribution of the work comes from the empirical evaluation of different similarity to assign the best sentiment scores to terms in the co-occurrence graph.

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Sentiment lexicon for sentiment analysis of Saudi dialect tweets

Cited by 44 publications

References 27 publications

Topic Modeling and Sentiment Analysis with LDA and NMF on Moroccan Tweets

Topic Modeling and Sentiment Analysis with LDA and NMF on Moroccan Tweets

Tracking And Understanding Public Reaction During COVID-19: Saudi Arabia As A Use Case

A Corpus Based Approach to Build Arabic Sentiment Lexicon

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