Time-sensitive Arabic multiword expressions extraction from social networks

Daoud, Daoud; Alkouz, Akram; Daoud, Mohammad

doi:10.1007/s10772-015-9315-3

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…In addition, wellknown statistical models like pointwise mutual information (PMI) and the log likelihood ratio (LLR) were utilised. Using an Arabic corpus, Daoud et al (2016) [16] described a method for extracting MWE. In the beginning, the researchers gathered 15.25 million Arabic tweets from 25 days of tweets.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification and extraction of multiword expressions from Hindi & Urdu language in natural language processing

2022

IJATEE

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Text can be translated from one language to another using statistical machine translation, but there are still gaps in the translations because of a lack of language resource material. Building a linguistic corpus necessarily requires the extraction of multiword expressions (MWE). MWE is a collection of words with idiomatic expression properties. However, due to its non-compositional meaning of distinctive words, identifying and extracting MWE is a timeconsuming task. In this case, an automated system has been developed for the extraction of MWEs from Hindi and Urdu language sources automatically. The entire process includes tagging, pattern matching, an identification algorithm, and the extraction of MWEs from the data. Tagging each word with a unique part of speech tag is used as an input to the pattern-matching algorithm. Using pattern matching, MWE tags of specific patterns were selected, and the algorithm for automatic MWE detection was built on top of that. The conditional random field (CRF++) model was used to automatically extract the MWEs from data. Confusion matrix was used to conduct the automated evaluation of thisproposed system. For Hindi and Urdu, the calculated overall accuracy is 96.82% and 96.62%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Identification and extraction of multiword expressions from Hindi & Urdu language in natural language processing

2022

IJATEE

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“…For analysis, we performed filtering of the tweets by a language detection software to remove any non-Arabic tweets in our dataset (Daoud et al, 2016).…”

Section: Datasetmentioning

confidence: 99%

Detecting and Ranking events in Twitter using Diversity Analysis

Daoud

2017

IJBIDM

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In Twitter and in other social media channels, detecting events is very important and has many applications. However, this task is very challenging because of the huge number of tweets that are posted every minute and the massive scale of the spamming activities. In this paper, we present an innovative approach for detecting events using data posted to Twitter. The proposed approach is based on the concept of user's attention by quantitatively modelling the diversity of hashtags using Shannon's index. Our method records the diversity values on an hourly basis time-series. Using statistical techniques, the method locates the intervals having diversity values that fall outside the range of forecasted ones (normal state). We also present the labelling and ranking techniques that are implemented in this research. Experimental results on a dataset consisting of 15 million Arabic tweets show that our proposed approach can effectively detect real-world events in Twitter.

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“…Analyzing some Arabic tweets reveals that the text holds many different surface structures for the same meaning [1].…”

Section: Introductionmentioning

confidence: 99%

Building a Sentiment Analysis System Using Automatically Generated Training Dataset

Daoud

El-Seoud

2020

Int. J. Onl. Eng.

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In this paper, we describe a methodology to develop a large training set for sentiment analysis automatically. We extract Arabic tweets and then annotates them for negativeness and positiveness sentiment without human intervention. These annotated tweets are used as a training data set to build our experimental sentiment analysis by using Naive Bayes algorithm and TF-IDF enhancement. The large size of training data for a highly inflected language is necessary to compensate for the sparseness nature of such languages. We present our techniques and explain our experimental system. We use 200 thousand annotated tweets to train our system. The evaluation shows that our sentiment analysis system has high precision and accuracy measures compared to existing ones.

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Time-sensitive Arabic multiword expressions extraction from social networks

Cited by 9 publications

References 8 publications

Identification and extraction of multiword expressions from Hindi & Urdu language in natural language processing

Identification and extraction of multiword expressions from Hindi & Urdu language in natural language processing

Detecting and Ranking events in Twitter using Diversity Analysis

Building a Sentiment Analysis System Using Automatically Generated Training Dataset

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